#data1 <- data %>% drop_na(case_control) %>% dyplr::select(-strive_id,-contains("scld")) %>%
 # mutate_at(.vars = potential_conf[-2],
  #          .funs = ~ as.character(.))
# 
# table1::table1(~.|case_control, data1)

Check percentage of NA for each PFAS

data_pfas<- data %>% dplyr::select(pfas_name, -contains("scld"))

## Stats----
pfas_na <- data_pfas %>% 
  pivot_longer(cols = pfas_name) %>%
  group_by(name) %>%
  dplyr::summarize(pct_na = sum(is.na(value))/dim(data_pfas)[1])%>%
  ungroup()%>%
  arrange(pct_na)

print(pfas_na, n=25) #n=25
## # A tibble: 25 × 2
##    name            pct_na
##    <chr>            <dbl>
##  1 pf_do_a          0    
##  2 pf_hp_a          0    
##  3 pf_hp_s          0    
##  4 pf_hx_a          0    
##  5 pf_hx_s          0    
##  6 pf_pe_a          0    
##  7 pf_pe_s          0    
##  8 pf_un_a          0    
##  9 pfba             0    
## 10 pfbs             0    
## 11 pfda             0    
## 12 pfna             0    
## 13 pfoa             0    
## 14 pfos             0    
## 15 x9cl_pf3ons      0.267
## 16 x8_2fts          0.939
## 17 hfpo_da          0.944
## 18 pfeesa           0.950
## 19 x6_2fts          0.979
## 20 x11cl_pf3o_ud_s  0.989
## 21 adona            0.992
## 22 nfdha            0.997
## 23 pfmba            0.997
## 24 pfmpa            0.997
## 25 x4_2fts          0.997
# Adding normalized PFAS
data_pfas_scd <- data_pfas %>% 
  mutate_at(.vars = names(data_pfas),
            .funs = list(scld = ~scale(.)%>% as.vector(.))) %>% dplyr::select(contains("scld"))

correlation_matrix <- data_pfas_scd %>%
  select_if(~sum(!is.na(.)) > 0) %>%  # Ensure there is at least some non-missing data
  cor(use = "pairwise.complete.obs")

print(correlation_matrix)
##                         pfba_scld  pf_pe_a_scld pf_hx_a_scld pf_hp_a_scld
## pfba_scld             1.000000000  0.0543499400 -0.050386280   0.25003054
## pf_pe_a_scld          0.054349940  1.0000000000  0.050463834  -0.05439341
## pf_hx_a_scld         -0.050386280  0.0504638340  1.000000000   0.14482447
## pf_hp_a_scld          0.250030540 -0.0543934086  0.144824473   1.00000000
## pfoa_scld            -0.057231673 -0.0798103861 -0.090128401   0.33935398
## pfna_scld            -0.066845973 -0.0598405991 -0.106040612   0.19186209
## pfda_scld             0.011123974  0.0047841577 -0.115760252   0.18264936
## pf_un_a_scld          0.021508401 -0.0002736169 -0.140203638   0.13481229
## pf_do_a_scld          0.092717740  0.0080579055 -0.090936382   0.14068137
## hfpo_da_scld          0.073148569  0.1847227651  0.009064506  -0.31529695
## pfbs_scld             0.212656172 -0.0300843635  0.011924920   0.48634102
## pf_pe_s_scld         -0.001061104 -0.0568650696 -0.010201890   0.47944494
## pf_hx_s_scld         -0.032329599 -0.0810383044 -0.071786764   0.29696176
## pf_hp_s_scld         -0.038415970 -0.0791630751 -0.142705592   0.22368108
## pfos_scld            -0.015922406 -0.0235359965 -0.130012978   0.17686502
## x6_2fts_scld         -0.403054453  0.3122922386 -0.274737535  -0.12810615
## x8_2fts_scld         -0.029653414  0.0351079929  0.505409321  -0.15938202
## adona_scld                     NA  0.8264867649 -0.466321371   0.79427736
## pfeesa_scld          -0.464329780 -0.3832481791  0.166024353  -0.08926641
## x9cl_pf3ons_scld     -0.015998696  0.0494809359 -0.006153293   0.02768365
## x11cl_pf3o_ud_s_scld           NA  0.6159388044  0.228687955   0.70768872
##                        pfoa_scld    pfna_scld    pfda_scld  pf_un_a_scld
## pfba_scld            -0.05723167 -0.066845973  0.011123974  0.0215084011
## pf_pe_a_scld         -0.07981039 -0.059840599  0.004784158 -0.0002736169
## pf_hx_a_scld         -0.09012840 -0.106040612 -0.115760252 -0.1402036384
## pf_hp_a_scld          0.33935398  0.191862093  0.182649363  0.1348122875
## pfoa_scld             1.00000000  0.763353190  0.531275533  0.3277988109
## pfna_scld             0.76335319  1.000000000  0.791980024  0.5617595563
## pfda_scld             0.53127553  0.791980024  1.000000000  0.8187452278
## pf_un_a_scld          0.32779881  0.561759556  0.818745228  1.0000000000
## pf_do_a_scld          0.22460958  0.404655974  0.724313889  0.7608520307
## hfpo_da_scld         -0.25073689 -0.140740646 -0.135958675 -0.1928757253
## pfbs_scld             0.07880220  0.046710633  0.073781847  0.0898649439
## pf_pe_s_scld          0.47473027  0.196594819  0.221581293  0.2237770503
## pf_hx_s_scld          0.53757395  0.296106399  0.215283880  0.2182507466
## pf_hp_s_scld          0.55092467  0.565898358  0.534993490  0.4842219935
## pfos_scld             0.44506674  0.611070725  0.757998359  0.7104051311
## x6_2fts_scld          0.21735843  0.245508321  0.204418331  0.0966787444
## x8_2fts_scld          0.15189179  0.006179123  0.105075259  0.0173320779
## adona_scld            0.98423933  0.800797338  0.994893541  0.9973266730
## pfeesa_scld          -0.23027405 -0.148790453 -0.270509659 -0.2709701487
## x9cl_pf3ons_scld      0.10327682  0.162233086  0.358111251  0.3474849412
## x11cl_pf3o_ud_s_scld  0.21369162  0.410018943  0.492054307  0.4262740527
##                      pf_do_a_scld hfpo_da_scld    pfbs_scld pf_pe_s_scld
## pfba_scld             0.092717740  0.073148569  0.212656172 -0.001061104
## pf_pe_a_scld          0.008057905  0.184722765 -0.030084364 -0.056865070
## pf_hx_a_scld         -0.090936382  0.009064506  0.011924920 -0.010201890
## pf_hp_a_scld          0.140681373 -0.315296951  0.486341016  0.479444944
## pfoa_scld             0.224609575 -0.250736889  0.078802197  0.474730274
## pfna_scld             0.404655974 -0.140740646  0.046710633  0.196594819
## pfda_scld             0.724313889 -0.135958675  0.073781847  0.221581293
## pf_un_a_scld          0.760852031 -0.192875725  0.089864944  0.223777050
## pf_do_a_scld          1.000000000  0.196778846  0.125017110  0.174840064
## hfpo_da_scld          0.196778846  1.000000000 -0.155043725 -0.111465998
## pfbs_scld             0.125017110 -0.155043725  1.000000000  0.135070072
## pf_pe_s_scld          0.174840064 -0.111465998  0.135070072  1.000000000
## pf_hx_s_scld          0.102323446 -0.146851282  0.054220398  0.672953480
## pf_hp_s_scld          0.364253692  0.166579594  0.065013044  0.457293728
## pfos_scld             0.622260918  0.074086233  0.071541404  0.319466233
## x6_2fts_scld         -0.035523245           NA -0.268907342  0.010578642
## x8_2fts_scld          0.027663920 -1.000000000 -0.299861487  0.225333198
## adona_scld            0.842731002           NA  0.999260081  0.960768923
## pfeesa_scld          -0.100721712           NA  0.180493414 -0.180934765
## x9cl_pf3ons_scld      0.184391389 -0.036500205 -0.009044973  0.179528111
## x11cl_pf3o_ud_s_scld  0.340938836           NA  0.757896416  0.743341943
##                      pf_hx_s_scld pf_hp_s_scld   pfos_scld x6_2fts_scld
## pfba_scld             -0.03232960  -0.03841597 -0.01592241  -0.40305445
## pf_pe_a_scld          -0.08103830  -0.07916308 -0.02353600   0.31229224
## pf_hx_a_scld          -0.07178676  -0.14270559 -0.13001298  -0.27473753
## pf_hp_a_scld           0.29696176   0.22368108  0.17686502  -0.12810615
## pfoa_scld              0.53757395   0.55092467  0.44506674   0.21735843
## pfna_scld              0.29610640   0.56589836  0.61107072   0.24550832
## pfda_scld              0.21528388   0.53499349  0.75799836   0.20441833
## pf_un_a_scld           0.21825075   0.48422199  0.71040513   0.09667874
## pf_do_a_scld           0.10232345   0.36425369  0.62226092  -0.03552324
## hfpo_da_scld          -0.14685128   0.16657959  0.07408623           NA
## pfbs_scld              0.05422040   0.06501304  0.07154140  -0.26890734
## pf_pe_s_scld           0.67295348   0.45729373  0.31946623   0.01057864
## pf_hx_s_scld           1.00000000   0.70293011  0.46909749   0.02076347
## pf_hp_s_scld           0.70293011   1.00000000  0.86330712   0.16638840
## pfos_scld              0.46909749   0.86330712  1.00000000   0.16197363
## x6_2fts_scld           0.02076347   0.16638840  0.16197363   1.00000000
## x8_2fts_scld           0.18584808   0.31338227  0.24384879           NA
## adona_scld             0.99625643   0.66158999  0.59389911           NA
## pfeesa_scld           -0.25296145  -0.31630560 -0.31206574           NA
## x9cl_pf3ons_scld       0.21782378   0.18051926  0.27195850  -0.06401844
## x11cl_pf3o_ud_s_scld   0.40193716   0.49731826  0.58009294           NA
##                      x8_2fts_scld adona_scld pfeesa_scld x9cl_pf3ons_scld
## pfba_scld            -0.029653414         NA -0.46432978     -0.015998696
## pf_pe_a_scld          0.035107993  0.8264868 -0.38324818      0.049480936
## pf_hx_a_scld          0.505409321 -0.4663214  0.16602435     -0.006153293
## pf_hp_a_scld         -0.159382021  0.7942774 -0.08926641      0.027683653
## pfoa_scld             0.151891788  0.9842393 -0.23027405      0.103276819
## pfna_scld             0.006179123  0.8007973 -0.14879045      0.162233086
## pfda_scld             0.105075259  0.9948935 -0.27050966      0.358111251
## pf_un_a_scld          0.017332078  0.9973267 -0.27097015      0.347484941
## pf_do_a_scld          0.027663920  0.8427310 -0.10072171      0.184391389
## hfpo_da_scld         -1.000000000         NA          NA     -0.036500205
## pfbs_scld            -0.299861487  0.9992601  0.18049341     -0.009044973
## pf_pe_s_scld          0.225333198  0.9607689 -0.18093476      0.179528111
## pf_hx_s_scld          0.185848083  0.9962564 -0.25296145      0.217823779
## pf_hp_s_scld          0.313382273  0.6615900 -0.31630560      0.180519256
## pfos_scld             0.243848793  0.5938991 -0.31206574      0.271958503
## x6_2fts_scld                   NA         NA          NA     -0.064018440
## x8_2fts_scld          1.000000000         NA          NA     -0.235970171
## adona_scld                     NA  1.0000000          NA     -1.000000000
## pfeesa_scld                    NA         NA  1.00000000     -0.002011390
## x9cl_pf3ons_scld     -0.235970171 -1.0000000 -0.00201139      1.000000000
## x11cl_pf3o_ud_s_scld           NA         NA          NA      0.993671174
##                      x11cl_pf3o_ud_s_scld
## pfba_scld                              NA
## pf_pe_a_scld                    0.6159388
## pf_hx_a_scld                    0.2286880
## pf_hp_a_scld                    0.7076887
## pfoa_scld                       0.2136916
## pfna_scld                       0.4100189
## pfda_scld                       0.4920543
## pf_un_a_scld                    0.4262741
## pf_do_a_scld                    0.3409388
## hfpo_da_scld                           NA
## pfbs_scld                       0.7578964
## pf_pe_s_scld                    0.7433419
## pf_hx_s_scld                    0.4019372
## pf_hp_s_scld                    0.4973183
## pfos_scld                       0.5800929
## x6_2fts_scld                           NA
## x8_2fts_scld                           NA
## adona_scld                             NA
## pfeesa_scld                            NA
## x9cl_pf3ons_scld                0.9936712
## x11cl_pf3o_ud_s_scld            1.0000000
correlation_long <- melt(correlation_matrix)

correlation_long <- correlation_long %>%
  mutate(keep = as.numeric(Var1) >= as.numeric(Var2)) %>%
  filter(keep) %>%
  dplyr::select(-keep)

heatmap_plot <- ggplot(data = correlation_long, aes(Var1, Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1, 1), space = "Lab", 
                       name="Correlation") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8, hjust = 1)) +
  coord_fixed() +
  labs(x = "PFAS", y = "PFAS", title = "Correlation Heatmap of PFAS Variables")

print(heatmap_plot)

##scaled 14 PFAS
data_pfas1 <- data %>% dplyr::select(contains("scld"))

correlation_matrix1 <- data_pfas1 %>%
  select_if(~sum(!is.na(.)) > 0) %>%
  cor(use = "pairwise.complete.obs")

correlation_long1 <- melt(correlation_matrix1)

correlation_long1 <- correlation_long1 %>%
  mutate(keep = as.numeric(Var1) >= as.numeric(Var2)) %>%
  filter(keep) %>%
  dplyr::select(-keep)

heatmap_plot1 <- ggplot(data = correlation_long1, aes(Var1, Var2, fill = value)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
                       midpoint = 0, limit = c(-1, 1), space = "Lab", 
                       name="Correlation") +
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 1, 
                                   size = 8, hjust = 1)) +
  coord_fixed() +
  labs(x = "PFAS", y = "PFAS", title = "Correlation Heatmap of Scaled PFAS Variables")

print(heatmap_plot1)

Descriptive stats of PFAS in overall paricipants

data_pfas<- data %>% dplyr::select(pfas_name_analysis)

## Stats----
(pfas_stats <- data_pfas %>% 
  pivot_longer(cols = pfas_name_analysis) %>%
  drop_na() %>%
  group_by(name) %>%
  dplyr::summarise(
    geometric_mean = jag2::fungm(value),
    sd = jag2::fungsd(value),
    min = min(value),
    max = max(value),
    percentile_25 = jag2::qntle_fxn(value, 0.25),
    percentile_50 = jag2::qntle_fxn(value, 0.5),
    percentile_75 = jag2::qntle_fxn(value, 0.75),
    percentile_90 = jag2::qntle_fxn(value, 0.90)
  ) %>% 
  ungroup())

Process of selecting outcome variables

#1. ALT
num_na_alt <- sum(is.na(data$alt_u_l))
cat("Print: the number of NA in ALT=", num_na_alt)
## Print: the number of NA in ALT= 29
cat("Print: percentage of NA in ALT=", (num_na_alt/dim(data)[1])*100)
## Print: percentage of NA in ALT= 7.671958
# 1-1. check the distribution of ALT
alt_clean <- na.omit(data$alt_u_l) # Remove missing values


min_alt <- min(alt_clean)# Calculate range
max_alt <- max(alt_clean)
mean_alt <- mean(alt_clean)# Calculate mean
sd_alt <- sd(alt_clean)# Calculate standard deviation



(data %>% dplyr::select(pfas_name_analysis, covars,race_final_label,
                  ethnicity,contains("sq_"),contains("alt"), 
                  contains("ast"), contains("trig"),cirrhosis,
                  case_control) %>%
  tbl_summary(by = case_control,
              type = all_continuous() ~ "continuous2",
              statistic = list(
                               all_continuous() ~ c("{mean} ({sd})",
                                                    "{median} ({p25}, {p75})"),
                               all_categorical() ~ "{n} ({p}%)")) %>%
  add_p(pvalue_fun = ~ style_pvalue(.x, digits = 2)) %>%
  add_overall() %>%
  add_n())
## 35 observations missing `case_control` have been removed. To include these observations, use `forcats::fct_na_value_to_level()` on `case_control` column before passing to `tbl_summary()`.
## There was an error in 'add_p()/add_difference()' for variable 'alt_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("7 - 55 U/L", "7 - 55 U/L", "7 - 55 U/L", : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'ast_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("5 - 34 U/L", "5 - 34 U/L", "5 - 34 U/L", : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'trig_normal_range', p-value omitted:
## Error in stats::chisq.test(x = c("0 - 149 mg/dL", "0 - 149 mg/dL", "0 - 149 mg/dL", : 'x' and 'y' must have at least 2 levels
Characteristic N Overall, N = 3431 Healthy, N = 2061 With cirrhosis, N = 1371 p-value2
pf_hx_s 343


0.003
    Mean (SD)
1.46 (1.60) 1.61 (1.71) 1.23 (1.39)
    Median (IQR)
1.05 (0.54, 1.74) 1.21 (0.61, 1.91) 0.86 (0.44, 1.46)
pfda 343


<0.001
    Mean (SD)
0.26 (0.33) 0.29 (0.35) 0.21 (0.31)
    Median (IQR)
0.16 (0.09, 0.27) 0.18 (0.11, 0.30) 0.12 (0.07, 0.23)
pfna 343


<0.001
    Mean (SD)
0.55 (0.54) 0.62 (0.58) 0.45 (0.45)
    Median (IQR)
0.42 (0.25, 0.65) 0.48 (0.32, 0.73) 0.34 (0.18, 0.54)
pfos 343


<0.001
    Mean (SD)
6.1 (6.5) 6.8 (6.5) 5.1 (6.5)
    Median (IQR)
4.2 (2.3, 7.1) 4.8 (2.9, 8.6) 3.4 (1.7, 6.1)
pf_hp_a 343


0.020
    Mean (SD)
0.07 (0.05) 0.06 (0.04) 0.08 (0.07)
    Median (IQR)
0.05 (0.03, 0.08) 0.05 (0.03, 0.07) 0.05 (0.03, 0.10)
pfbs 343


0.94
    Mean (SD)
0.050 (0.110) 0.044 (0.082) 0.057 (0.142)
    Median (IQR)
0.029 (0.020, 0.047) 0.028 (0.020, 0.044) 0.029 (0.018, 0.049)
pfoa 343


<0.001
    Mean (SD)
1.44 (1.47) 1.63 (1.67) 1.16 (1.03)
    Median (IQR)
1.08 (0.63, 1.85) 1.21 (0.75, 2.03) 0.91 (0.48, 1.52)
pf_pe_a 343


0.023
    Mean (SD)
0.049 (0.032) 0.051 (0.036) 0.046 (0.025)
    Median (IQR)
0.042 (0.034, 0.053) 0.043 (0.036, 0.054) 0.037 (0.032, 0.052)
pf_un_a 343


<0.001
    Mean (SD)
0.13 (0.15) 0.15 (0.15) 0.09 (0.14)
    Median (IQR)
0.08 (0.04, 0.15) 0.10 (0.05, 0.19) 0.06 (0.03, 0.10)
pf_hp_s 343


<0.001
    Mean (SD)
0.20 (0.17) 0.22 (0.17) 0.17 (0.16)
    Median (IQR)
0.15 (0.08, 0.27) 0.16 (0.10, 0.29) 0.13 (0.06, 0.23)
pf_do_a 343


0.001
    Mean (SD)
0.021 (0.035) 0.023 (0.033) 0.019 (0.038)
    Median (IQR)
0.012 (0.007, 0.021) 0.014 (0.007, 0.024) 0.009 (0.005, 0.016)
pf_pe_s 343


0.036
    Mean (SD)
0.028 (0.031) 0.030 (0.035) 0.024 (0.024)
    Median (IQR)
0.019 (0.011, 0.033) 0.020 (0.013, 0.036) 0.018 (0.010, 0.030)
pf_hx_a 343


0.004
    Mean (SD)
0.011 (0.011) 0.010 (0.010) 0.013 (0.012)
    Median (IQR)
0.010 (0.002, 0.014) 0.009 (0.002, 0.013) 0.011 (0.002, 0.017)
pfba 343


0.092
    Mean (SD)
0.03 (0.07) 0.02 (0.05) 0.04 (0.09)
    Median (IQR)
0.00 (0.00, 0.00) 0.00 (0.00, 0.00) 0.00 (0.00, 0.00)
source 343


<0.001
    DUKE
86 (25%) 7 (3.4%) 79 (58%)
    Emory
92 (27%) 54 (26%) 38 (28%)
    NCSU
147 (43%) 145 (70%) 2 (1.5%)
    UNC
18 (5.2%) 0 (0%) 18 (13%)
age_at_enrollment 331


0.29
    Mean (SD)
59 (10) 59 (10) 60 (8)
    Median (IQR)
59 (52, 67) 59 (51, 68) 60 (55, 67)
    Unknown
12 6 6
sex 343


<0.001
    Female
219 (64%) 148 (72%) 71 (52%)
    Male
124 (36%) 58 (28%) 66 (48%)
race_eth_label 343


<0.001
    Hispanic
12 (3.5%) 6 (2.9%) 6 (4.4%)
    NHB
94 (27%) 76 (37%) 18 (13%)
    NHO
15 (4.4%) 10 (4.9%) 5 (3.6%)
    NHW
208 (61%) 105 (51%) 103 (75%)
    Unknown/Not Reported
14 (4.1%) 9 (4.4%) 5 (3.6%)
race_final_label 343


<0.001
    American Indian
2 (0.6%) 0 (0%) 2 (1.5%)
    American Indian/Alaskan Native
4 (1.2%) 4 (1.9%) 0 (0%)
    Asian
3 (0.9%) 1 (0.5%) 2 (1.5%)
    Asian/Pacific Islander
6 (1.7%) 5 (2.4%) 1 (0.7%)
    Black
98 (29%) 80 (39%) 18 (13%)
    Other
7 (2.0%) 6 (2.9%) 1 (0.7%)
    White
223 (65%) 110 (53%) 113 (82%)
ethnicity 343


0.81
    Hispanic
12 (3.5%) 6 (2.9%) 6 (4.4%)
    Not Hispanic
317 (92%) 191 (93%) 126 (92%)
    Unknown/Not Reported
14 (4.1%) 9 (4.4%) 5 (3.6%)
rural 343


<0.001
    Living in Metro area
272 (79%) 184 (89%) 88 (64%)
    Living in rural area
31 (9.0%) 10 (4.9%) 21 (15%)
    Unknown/Not Reported
40 (12%) 12 (5.8%) 28 (20%)
smoking 343


<0.001
    Don't smoke or use vape
236 (69%) 157 (76%) 79 (58%)
    Smoke or use vape
35 (10%) 20 (9.7%) 15 (11%)
    Unknown/Not Reported
72 (21%) 29 (14%) 43 (31%)
sq_drink_alcohol 343


<0.001
    No, former drinker (stopped)
78 (23%) 26 (13%) 52 (38%)
    No, never drinker
84 (24%) 49 (24%) 35 (26%)
    Unknown/Not Reported
73 (21%) 30 (15%) 43 (31%)
    Yes, current drinker
108 (31%) 101 (49%) 7 (5.1%)
sq_average_drink_per_day 343


<0.001
    1-2 alcoholic drinks per day
18 (5.2%) 17 (8.3%) 1 (0.7%)
    3-4 alcoholic drinks per day
6 (1.7%) 5 (2.4%) 1 (0.7%)
    Less than 1 alcoholic drink per day
84 (24%) 79 (38%) 5 (3.6%)
    Unknown/Not Reported
235 (69%) 105 (51%) 130 (95%)
sq_self_hep_b 343


<0.001
    No
248 (72%) 165 (80%) 83 (61%)
    Unknown/Not Reported
78 (23%) 32 (16%) 46 (34%)
    Yes
17 (5.0%) 9 (4.4%) 8 (5.8%)
sq_self_hep_c 343


<0.001
    No
248 (72%) 173 (84%) 75 (55%)
    Unknown/Not Reported
77 (22%) 31 (15%) 46 (34%)
    Yes
18 (5.2%) 2 (1.0%) 16 (12%)
supp_meds_tylenol 343


0.012
    No
7 (2.0%) 7 (3.4%) 0 (0%)
    Unknown/Not Reported
332 (97%) 195 (95%) 137 (100%)
    Yes
4 (1.2%) 4 (1.9%) 0 (0%)
supp_meds_steroids 343


0.013
    No
9 (2.6%) 9 (4.4%) 0 (0%)
    Unknown/Not Reported
333 (97%) 196 (95%) 137 (100%)
    Yes
1 (0.3%) 1 (0.5%) 0 (0%)
sq_water_well 343


0.004
    No
179 (52%) 120 (58%) 59 (43%)
    Unknown/Not Reported
108 (31%) 62 (30%) 46 (34%)
    Yes
56 (16%) 24 (12%) 32 (23%)
sq_water_tap_unfiltered 343


0.064
    No
85 (25%) 48 (23%) 37 (27%)
    Unknown/Not Reported
92 (27%) 48 (23%) 44 (32%)
    Yes
166 (48%) 110 (53%) 56 (41%)
sq_water_house_filtration 343


0.98
    No
195 (57%) 118 (57%) 77 (56%)
    Unknown/Not Reported
113 (33%) 67 (33%) 46 (34%)
    Yes
35 (10%) 21 (10%) 14 (10%)
sq_water_faucet_filter 343


0.60
    No
133 (39%) 81 (39%) 52 (38%)
    Unknown/Not Reported
105 (31%) 59 (29%) 46 (34%)
    Yes
105 (31%) 66 (32%) 39 (28%)
sq_water_charcoal_filter 343


0.90
    No
178 (52%) 109 (53%) 69 (50%)
    Unknown/Not Reported
112 (33%) 66 (32%) 46 (34%)
    Yes
53 (15%) 31 (15%) 22 (16%)
sq_water_bottled 343


0.25
    No
80 (23%) 54 (26%) 26 (19%)
    Unknown/Not Reported
99 (29%) 55 (27%) 44 (32%)
    Yes
164 (48%) 97 (47%) 67 (49%)
sq_water_none 343


0.048
    No
210 (61%) 132 (64%) 78 (57%)
    Unknown/Not Reported
115 (34%) 68 (33%) 47 (34%)
    Yes
18 (5.2%) 6 (2.9%) 12 (8.8%)
sq_water_other_type 343


0.61
    No
206 (60%) 128 (62%) 78 (57%)
    Unknown/Not Reported
110 (32%) 62 (30%) 48 (35%)
    Yes
27 (7.9%) 16 (7.8%) 11 (8.0%)
sq_water_dont_know 343


0.79
    No
210 (61%) 129 (63%) 81 (59%)
    Unknown/Not Reported
115 (34%) 67 (33%) 48 (35%)
    Yes
18 (5.2%) 10 (4.9%) 8 (5.8%)
alt_u_l 316


<0.001
    Mean (SD)
16 (15) 15 (17) 17 (12)
    Median (IQR)
12 (8, 19) 11 (8, 16) 15 (10, 21)
    Unknown
27 19 8
log_alt 316


<0.001
    Mean (SD)
2.52 (0.65) 2.44 (0.65) 2.65 (0.63)
    Median (IQR)
2.48 (2.08, 2.94) 2.40 (2.08, 2.77) 2.71 (2.30, 3.04)
    Unknown
27 19 8
alt_normal_range 316



    7 - 55 U/L
316 (100%) 187 (100%) 129 (100%)
    Unknown
27 19 8
alt_flags 316


0.44
    <
19 (6.0%) 12 (6.4%) 7 (5.4%)
    HIGH
8 (2.5%) 6 (3.2%) 2 (1.6%)
    LOW
16 (5.1%) 12 (6.4%) 4 (3.1%)
    Norm
273 (86%) 157 (84%) 116 (90%)
    Unknown
27 19 8
AST/ALT 316


0.002
    Mean (SD)
2.82 (1.93) 2.59 (1.59) 3.15 (2.30)
    Median (IQR)
2.29 (1.77, 3.16) 2.18 (1.73, 2.86) 2.44 (2.00, 3.50)
    Unknown
27 19 8
alt_cat1 316


0.062
    High
47 (15%) 22 (12%) 25 (19%)
    Norm
269 (85%) 165 (88%) 104 (81%)
    Unknown
27 19 8
alt_cat2 316


0.30
    High
28 (8.9%) 14 (7.5%) 14 (11%)
    Norm
288 (91%) 173 (93%) 115 (89%)
    Unknown
27 19 8
ast_u_l 316


<0.001
    Mean (SD)
38 (32) 33 (31) 45 (33)
    Median (IQR)
27 (20, 42) 21 (18, 32) 36 (26, 49)
    Unknown
27 19 8
log_ast 316


<0.001
    Mean (SD)
3.41 (0.61) 3.25 (0.59) 3.64 (0.57)
    Median (IQR)
3.28 (3.00, 3.74) 3.04 (2.89, 3.47) 3.58 (3.26, 3.89)
    Unknown
27 19 8
ast_normal_range 316



    5 - 34 U/L
316 (100%) 187 (100%) 129 (100%)
    Unknown
27 19 8
ast_flags 316


<0.001
    HIGH
113 (36%) 44 (24%) 69 (53%)
    Norm
203 (64%) 143 (76%) 60 (47%)
    Unknown
27 19 8
ast_cat1 316


<0.001
    High
203 (64%) 102 (55%) 101 (78%)
    Norm
113 (36%) 85 (45%) 28 (22%)
    Unknown
27 19 8
ast_cat2 316


<0.001
    High
157 (50%) 67 (36%) 90 (70%)
    Norm
159 (50%) 120 (64%) 39 (30%)
    Unknown
27 19 8
trig_mg_d_l 322


0.037
    Mean (SD)
135 (79) 143 (83) 123 (71)
    Median (IQR)
112 (79, 165) 117 (82, 176) 105 (77, 154)
    Unknown
21 15 6
log_trig 322


0.037
    Mean (SD)
4.76 (0.54) 4.82 (0.52) 4.67 (0.55)
    Median (IQR)
4.72 (4.37, 5.10) 4.76 (4.40, 5.17) 4.65 (4.34, 5.03)
    Unknown
21 15 6
trig_normal_range 322



    0 - 149 mg/dL
322 (100%) 191 (100%) 131 (100%)
    Unknown
21 15 6
trig_flags 322


0.093
    HIGH
103 (32%) 68 (36%) 35 (27%)
    Norm
219 (68%) 123 (64%) 96 (73%)
    Unknown
21 15 6
cirrhosis 316


0.15
    Healthy
8 (2.5%) 7 (3.7%) 1 (0.8%)
    With cirrhosis
308 (97%) 180 (96%) 128 (99%)
    Unknown
27 19 8
1 n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test

Correlation between PFAS and potential confounders

covars <- c("source", "age_at_enrollment","sex", 
            "race_eth_label", "race_final_label","ethnicity",
            "rural", "smoking","sq_drink_alcohol","sq_average_drink_per_day","sq_self_hep_b",
            "sq_self_hep_c","supp_meds_tylenol","supp_meds_steroids","sq_water_well",
            "sq_water_tap_unfiltered","sq_water_house_filtration", "sq_water_faucet_filter",
            "sq_water_charcoal_filter","sq_water_bottled","sq_water_none","sq_water_other_type")




cat("Print: range of ALT=", min_alt," and ",max_alt, "\n")
## Print: range of ALT= 3  and  186
cat("Print: mean and SD of ALT=", mean_alt," and ", sd_alt, "\n")
## Print: mean and SD of ALT= 15.43266  and  14.65991
# Redraw the plot with extended axis limits
plotNormalHistogram(alt_clean, 
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of ALT(u/l)",
                    xlab = "ALT(u/l)",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3
                    )
              
density_data <- density(alt_clean)# Calculate density

hist_data <- hist(alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(alt_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

# 1-2. check the distribution of ln(ALT), based on the distribution of ALT, which needs natural log transformation.

num_na_ln_alt <- sum(is.na(data$log_alt)) #check the na
cat("Print: the number of NA in Ln ALT=", num_na_ln_alt, "\n")
## Print: the number of NA in Ln ALT= 29
cat("Print: percentage of NA in Ln ALT=", (num_na_ln_alt/dim(data)[1])*100,"\n")
## Print: percentage of NA in Ln ALT= 7.671958
ln_alt_clean <- na.omit(data$log_alt) # Remove missing values

min_ln_alt <- min(ln_alt_clean)# Calculate range
max_ln_alt <- max(ln_alt_clean)
mean_ln_alt <- mean(ln_alt_clean)# Calculate mean
sd_ln_alt <- sd(ln_alt_clean)# Calculate standard deviation

cat("Print: range of ln ALT=", min_ln_alt,max_ln_alt, "\n")
## Print: range of ln ALT= 1.098612 5.225747
cat("Print: mean and SD of ln ALT=", mean_ln_alt," and ", sd_ln_alt, "\n")
## Print: mean and SD of ln ALT= 2.514251  and  0.6316953
plotNormalHistogram(ln_alt_clean, # Plot histogram with normal distribution line
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of natural log ALT(u/l)",
                    xlab = "Natural log of ALT(u/l)",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3)

density_data <- density(ln_alt_clean)# Calculate density

hist_data <- hist(ln_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_alt_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

#2. AST
num_na_ast <- sum(is.na(data$ast_u_l))
cat("Print: the number of NA in AST=", num_na_ast)
## Print: the number of NA in AST= 29
cat("Print: percentage of NA in AST=", (num_na_ast/dim(data)[1])*100)
## Print: percentage of NA in AST= 7.671958
# 2-1. check the distribution of ast
ast_clean <- na.omit(data$ast_u_l) # Remove missing values

min_ast <- min(ast_clean)# Calculate range
max_ast <- max(ast_clean)
mean_ast <- mean(ast_clean)# Calculate mean
sd_ast <- sd(ast_clean)# Calculate standard deviation

cat("Print: range of AST=", min_ast," and ", max_ast, "\n")
## Print: range of AST= 7  and  227
cat("Print: mean and SD of AST=", mean_ast," and ", sd_ast, "\n")
## Print: mean and SD of AST= 36.41261  and  30.77582
# Redraw the plot with extended axis limits
plotNormalHistogram(ast_clean, 
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of AST(u/l)",
                    xlab = "AST(u/l)",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3
                    )
              
density_data <- density(ast_clean)# Calculate density

hist_data <- hist(ast_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ast_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

# 2-2. check the distribution of ln(ast), based on the distribution of ast, which needs natural log transformation.

num_na_ln_ast <- sum(is.na(data$log_ast)) #check the na
cat("Print: the number of NA in Ln AST=", num_na_ln_ast, "\n")
## Print: the number of NA in Ln AST= 29
cat("Print: percentage of NA in Ln AST=", (num_na_ln_ast/dim(data)[1])*100,"\n")
## Print: percentage of NA in Ln AST= 7.671958
ln_ast_clean <- na.omit(data$log_ast) # Remove missing values

min_ln_ast <- min(ln_ast_clean)# Calculate range
max_ln_ast <- max(ln_ast_clean)
mean_ln_ast <- mean(ln_ast_clean)# Calculate mean
sd_ln_ast <- sd(ln_ast_clean)# Calculate standard deviation

cat("Print: range of ln AST=", min_ln_ast," and ", max_ln_ast, "\n")
## Print: range of ln AST= 1.94591  and  5.42495
cat("Print: mean and SD of ln AST=", mean_ln_ast," and ", sd_ln_ast, "\n")
## Print: mean and SD of ln AST= 3.383119  and  0.5919342
plotNormalHistogram(ln_ast_clean, # Plot histogram with normal distribution line
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of natural log AST(u/l)",
                    xlab = "Natural log of AST(u/l)",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3)

density_data <- density(ln_ast_clean)# Calculate density

hist_data <- hist(ln_ast_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_ast_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

#3. AST/ALT ratio
num_na_ast_alt_ratio <- sum(is.na(data$`AST/ALT`))
cat("Print: the number of NA in AST/ALT ratio=", num_na_ast_alt_ratio)
## Print: the number of NA in AST/ALT ratio= 29
cat("Print: percentage of NA in AST/ALT ratio=", (num_na_ast_alt_ratio/dim(data)[1])*100)
## Print: percentage of NA in AST/ALT ratio= 7.671958
#3-1. check the distribution of ast
ast_alt_clean <- na.omit(data$`AST/ALT`) # Remove missing values

min_ast_alt <- min(ast_alt_clean)# Calculate range
max_ast_alt <- max(ast_alt_clean)
mean_ast_alt <- mean(ast_alt_clean)# Calculate mean
sd_ast_alt <- sd(ast_alt_clean)# Calculate standard deviation

cat("Print: range of AST/ALT ratio=", min_ast_alt," and ", max_ast_alt, "\n")
## Print: range of AST/ALT ratio= 0.6236559  and  16
cat("Print: mean and SD of AST/ALT ratio=", mean_ast_alt," and ", sd_ast_alt, "\n")
## Print: mean and SD of AST/ALT ratio= 2.744878  and  1.856091
# Redraw the plot with extended axis limits
plotNormalHistogram(ast_alt_clean, 
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of AST/ALT ratio",
                    xlab = "AST/ALT ratio",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3
                    )
              
density_data <- density(ast_alt_clean)# Calculate density

hist_data <- hist(ast_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ast_alt_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

# 3-2. check the distribution of ln(ast/alt), based on the distribution of ast/alt ratio, which needs natural log transformation.
num_na_ln_ast_alt <- sum(is.na(data$log_ast_alt)) #check the na
cat("Print: the number of NA in Ln AST/ALT=", num_na_ln_ast_alt, "\n")
## Print: the number of NA in Ln AST/ALT= 29
cat("Print: percentage of NA in Ln AST/ALT=", (num_na_ln_ast_alt/dim(data)[1])*100,"\n")
## Print: percentage of NA in Ln AST/ALT= 7.671958
ln_ast_alt_clean <- na.omit(data$log_ast_alt) # Remove missing values

min_ln_ast_alt <- min(ln_ast_alt_clean)# Calculate range
max_ln_ast_alt <- max(ln_ast_alt_clean)
mean_ln_ast_alt <- mean(ln_ast_alt_clean)# Calculate mean
sd_ln_ast_alt <- sd(ln_ast_alt_clean)# Calculate standard deviation

cat("Print: range of ln AST/ALT=", min_ln_ast,max_ln_ast_alt, "\n")
## Print: range of ln AST/ALT= 1.94591 2.772589
cat("Print: mean and SD of ln AST/ALT=", mean_ln_ast," and ", sd_ln_ast_alt, "\n")
## Print: mean and SD of ln AST/ALT= 3.383119  and  0.4937588
plotNormalHistogram(ln_ast_alt_clean, # Plot histogram with normal distribution line
                    prob = FALSE, 
                    col = "white", 
                    border = "black", 
                    main = "Distribution of natural log AST/ALT ratio",
                    xlab = "Natural log of AST/ALT ratio",
                    ylab = "Number of samples",
                    linecol = "red", 
                    lwd = 3)

density_data <- density(ln_ast_alt_clean)# Calculate density

hist_data <- hist(ln_ast_alt_clean, plot = FALSE) # Create the histogram to get the breaks for scaling the density

density_scaled <- density_data$y * diff(hist_data$mids[1:2]) * length(ln_ast_alt_clean) # Scale density values to match the histogram frequency

# Add density lines
lines(density_data$x, density_scaled, col = "blue", lwd = 2)

# Add legend
legend("topright", 
       legend = c("Normal Distribution", "Density"), 
       col = c("red", "blue"), 
       lwd = 3, 
       cex = 0.8, 
       bty = "n")

# 4. correlation between natural ln ast, ln alt, and ln ast/alt ratio by sex

# 4-1. version 1
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'sex')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'sex')

custom_plot <- function(data, mapping, ...) {#Define the custom plot function for the lower panel
  ggplot(data, mapping) +
    geom_point(aes(color = sex), ...) +
    geom_smooth(aes(color = sex), method = "lm", se = FALSE, ...) # Separate regression lines by sex
}
p <- ggpairs(data_renamed,# Generate the ggpairs plot with the custom plot function
             columns = 1:3,  # Only use the numeric columns for the pairs plot
             aes(color = sex),  # Color points by sex
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()
print(p)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).

#4-2. version2
custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = sex), ...) +
    geom_smooth(aes(color = sex), method = "lm", se = FALSE, ...)
}
p <- ggpairs(data_renamed, 
             aes(color = sex),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB",  "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
print(p)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5. correlation between natural ln ast, ln alt, and ln ast/alt ratio by categorical endpoints

# 5-1. AST cat1: cutoff 29 for Male and 19 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'ast_cat1')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'lowest cut AST')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `lowest cut AST`), ...) +
    geom_smooth(aes(color = `lowest cut AST`), method = "lm", se = FALSE, ...)
}
p1 <- ggpairs(data_renamed, 
             aes(color = `lowest cut AST`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p1)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-2. AST cat2: cutoff 33 for Male and 25 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'ast_cat2')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'highest cut AST')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `highest cut AST`), ...) +
    geom_smooth(aes(color = `highest cut AST`), method = "lm", se = FALSE, ...)
}
p2 <- ggpairs(data_renamed, 
             aes(color = `highest cut AST`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p2)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-3. ALT cat1: cutoff 29 for Male and 19 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'alt_cat1')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'lowest cut ALT')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `lowest cut ALT`), ...) +
    geom_smooth(aes(color = `lowest cut ALT`), method = "lm", se = FALSE, ...)
}
p3 <- ggpairs(data_renamed, 
             aes(color = `lowest cut ALT`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p3)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-4. ALT cat2: cutoff 33 for Male and 25 for female
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'alt_cat2')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'highest cut ALT')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `highest cut ALT`), ...) +
    geom_smooth(aes(color = `highest cut ALT`), method = "lm", se = FALSE, ...)
}
p4 <- ggpairs(data_renamed, 
             aes(color = `highest cut ALT`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p4)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-5. cirrhosis
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'cirrhosis')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'cirrhosis')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `cirrhosis`), ...) +
    geom_smooth(aes(color = `cirrhosis`), method = "lm", se = FALSE, ...)
}

p5 <- ggpairs(data_renamed, 
             aes(color = `cirrhosis`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p5)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 29 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-6. case_control

# 5-6-1. Total ppl
data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')

custom_plot <- function(data, mapping, ...) {
  ggplot(data, mapping) +
    geom_point(aes(color = `Cirrhosis diagnosed`), ...) +
    geom_smooth(aes(color = `Cirrhosis diagnosed`), method = "lm", se = FALSE, ...)
}

p6 <- ggpairs(data_renamed, 
             aes(color = `Cirrhosis diagnosed`),
             lower = list(continuous = custom_plot),
             upper = list(continuous = wrap("cor", size = 4))) +
  theme_bw()

for(i in 1:p$nrow) {# Customize color scales manually
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
      scale_fill_manual(values=c("#00AFBB", "#FC4E07")) +
      scale_color_manual(values=c("#00AFBB", "#FC4E07"))  
  }
}
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p6)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-6-2. After excluding NCSU data
# Before analyzing this, subsetting excluding NCSU data (182 ppl, 48.1%)
#(dim(data[data$source=='NCSU',])[1]/dim(data)[1])*100
data_ex_ncsu <- data[data$source=='NCSU',]

data_renamed <- data_ex_ncsu[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')

# 6. A frequency table of categorical outcomes

# 6-1. Total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                             levels = c("Healthy", "Cirrhosis"),
                                             labels = c("Normal", "Abnormal"))


frequency_tables <- lapply(data_renamed, function(x) {
  tbl <- as.data.frame(table(x))
  colnames(tbl) <- c("Category", "Frequency")
  return(tbl)
})

combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
  df <- frequency_tables[[name]]
  df$Variable <- name
  return(df)
}))

combined_freq_df <- combined_freq_df %>%
  pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
  arrange(Category)

combined_freq_df <- combined_freq_df %>%
  dplyr::select(Category, everything())

combined_freq_df$Category <- as.character(combined_freq_df$Category)

total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)

combined_freq_df <- rbind(combined_freq_df, total_row)

fancy_table1 <- flextable(combined_freq_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")

fancy_table1 <- set_caption(fancy_table1, caption = "Frequency tables for categorical endpoints in total ppl (N=349)")

fancy_table1 <- fontsize(fancy_table1, size = 8, part = "body") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
fancy_table1 <- border(fancy_table1, i = (nrow(fancy_table1$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table1 <- align(fancy_table1, align = "center", part = "body")

print(fancy_table1)
## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed` 
## header has 1 row(s) 
## body has 3 row(s) 
## original dataset sample: 
##   Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal            222             165             49              28
## 2   Normal            127             184            300             321
## 3    Total            349             349            349             349
##   cirrhosis cirrhosis diagnosed
## 1       341                 137
## 2         8                 206
## 3       349                 343
# 6-2. After excluding Total ppl
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                             levels = c("Healthy", "Cirrhosis"),
                                             labels = c("Normal", "Abnormal"))


frequency_tables <- lapply(data_renamed, function(x) {
  tbl <- as.data.frame(table(x))
  colnames(tbl) <- c("Category", "Frequency")
  return(tbl)
})

combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
  df <- frequency_tables[[name]]
  df$Variable <- name
  return(df)
}))

combined_freq_df <- combined_freq_df %>%
  pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
  arrange(Category)

combined_freq_df <- combined_freq_df %>%
  dplyr::select(Category, everything())

combined_freq_df$Category <- as.character(combined_freq_df$Category)

total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)

combined_freq_df <- rbind(combined_freq_df, total_row)

fancy_table2 <- flextable(combined_freq_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")

fancy_table2 <- set_caption(fancy_table2, caption = "Frequency tables for categorical endpoints after exlcuding NCSU (N=173)")

fancy_table2 <- fontsize(fancy_table2, size = 8, part = "body") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
fancy_table2 <- border(fancy_table2, i = (nrow(fancy_table2$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table2 <- align(fancy_table2, align = "center", part = "body")

print(fancy_table2)
## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed` 
## header has 1 row(s) 
## body has 3 row(s) 
## original dataset sample: 
##   Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal             88              43             10               5
## 2   Normal             85             130            163             168
## 3    Total            173             173            173             173
##   cirrhosis cirrhosis diagnosed
## 1       170                   2
## 2         3                 145
## 3       173                 147
# Create a new Word document
doc <- read_docx()

# Add the first flextable
doc <- body_add_flextable(doc, value = fancy_table1)

# Add a page break (optional) or additional spacing
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")

# Add the second flextable
doc <- body_add_flextable(doc, value = fancy_table2)

# Save the document
print(doc, target = "Frequency tables of categorical outcome vars.docx")

# 7. Association between cat endpoints 
# 7-1 through correlation matrix
# 7-1-1. total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>% 
     cor(use="pairwise.complete.obs")



# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix, 
           show.diag=FALSE, 
           type="lower", 
           lab=TRUE, 
           lab_size=2,
           title="Correlation matrix of categorical outcomes in total ppl (N=349)") +  # Title of the plot
        theme(
          plot.title = element_text(size = 10), # Title font size
          axis.text.x = element_text(size = 8), # X-axis title font size
          axis.text.y = element_text(size = 8)  # Y-axis title font size
        )

print(plot6)

# 7-1-2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>% 
     cor(use="pairwise.complete.obs")


# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix, 
           show.diag=FALSE, 
           type="lower", 
           lab=TRUE, 
           lab_size=2,
           title="Correlation matrix of categorical outcomes after excluding NCSU data (N=173)") +  # Title of the plot
        theme(
          plot.title = element_text(size = 10), # Title font size
          axis.text.x = element_text(size = 8), # X-axis title font size
          axis.text.y = element_text(size = 8)  # Y-axis title font size
        )

print(plot6)

###########Tabulate

# 1. total ppl
# Create dummy variables
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")

# Function to calculate p-values for correlations
cor.mtest <- function(mat, ...) {
  mat <- as.matrix(mat)
  n <- ncol(mat)
  p.mat <- matrix(NA, n, n)
  diag(p.mat) <- 0
  for (i in 1:(n - 1)) {
    for (j in (i + 1):n) {
      tmp <- cor.test(mat[, i], mat[, j], ...)
      p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
    }
  }
  colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
  p.mat
}

# Calculate p-values
p.mat <- cor.mtest(model_matrix)

# Convert correlation matrix and p-value matrix to long format
cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)

# Rename columns for merging
colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")

# Merge correlation and p-value data frames
plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))

# Check the structure and content of the merged data frame
print("Structure of plot_df:")
## [1] "Structure of plot_df:"
str(plot_df)
## 'data.frame':    49 obs. of  4 variables:
##  $ Var1       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
##  $ Var2       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
##  $ correlation: num  1 0.0582 0.3336 0.1052 -0.2435 ...
##  $ pvalue     : num  0 0.30221339661 0.00000000118 0.06181134671 0.000011963 ...
print("First few rows of plot_df:")
## [1] "First few rows of plot_df:"
head(plot_df)
# 5-6-1. Total ppl data_renamed <- data[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')] colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed') custom_plot <- function(data, mapping, ...) { ggplot(data, mapping) + geom_point(aes(color = `Cirrhosis diagnosed`), ...) + geom_smooth(aes(color = `Cirrhosis diagnosed`), method = "lm", se = FALSE, ...) } p6 <- ggpairs(data_renamed, aes(color = `Cirrhosis diagnosed`), lower = list(continuous = custom_plot), upper = list(continuous = wrap("cor", size = 4))) + theme_bw() for(i in 1:p$nrow) {# Customize color scales manually for(j in 1:p$ncol){ p[i,j] <- p[i,j] + scale_fill_manual(values=c("#00AFBB", "#FC4E07")) + scale_color_manual(values=c("#00AFBB", "#FC4E07")) } }
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
print(p6)
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 62 rows containing missing values
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 29 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_density()`).
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 29 rows containing non-finite outside the scale range
## (`stat_bin()`).

# 5-6-2. After excluding NCSU data
# Before analyzing this, subsetting excluding NCSU data (182 ppl, 48.1%)
#(dim(data[data$source=='NCSU',])[1]/dim(data)[1])*100
data_ex_ncsu <- data[data$source=='NCSU',]

data_renamed <- data_ex_ncsu[, c('log_ast_alt', 'log_ast', 'log_alt', 'case_control')]
colnames(data_renamed) <- c('natural log AST/ALT', 'natural log AST', 'natural log ALT', 'Cirrhosis diagnosed')

# 6. A frequency table of categorical outcomes

# 6-1. Total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                             levels = c("Healthy", "Cirrhosis"),
                                             labels = c("Normal", "Abnormal"))


frequency_tables <- lapply(data_renamed, function(x) {
  tbl <- as.data.frame(table(x))
  colnames(tbl) <- c("Category", "Frequency")
  return(tbl)
})

combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
  df <- frequency_tables[[name]]
  df$Variable <- name
  return(df)
}))

combined_freq_df <- combined_freq_df %>%
  pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
  arrange(Category)

combined_freq_df <- combined_freq_df %>%
  dplyr::select(Category, everything())

combined_freq_df$Category <- as.character(combined_freq_df$Category)

total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)

combined_freq_df <- rbind(combined_freq_df, total_row)

fancy_table1 <- flextable(combined_freq_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")

fancy_table1 <- set_caption(fancy_table1, caption = "Frequency tables for categorical endpoints in total ppl (N=349)")

fancy_table1 <- fontsize(fancy_table1, size = 8, part = "body") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header
fancy_table1 <- border(fancy_table1, i = (nrow(fancy_table1$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table1 <- align(fancy_table1, align = "center", part = "body")

print(fancy_table1)
## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed` 
## header has 1 row(s) 
## body has 3 row(s) 
## original dataset sample: 
##   Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal            222             165             49              28
## 2   Normal            127             184            300             321
## 3    Total            349             349            349             349
##   cirrhosis cirrhosis diagnosed
## 1       341                 137
## 2         8                 206
## 3       349                 343
# 6-2. After excluding Total ppl
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                             levels = c("Healthy", "Cirrhosis"),
                                             labels = c("Normal", "Abnormal"))


frequency_tables <- lapply(data_renamed, function(x) {
  tbl <- as.data.frame(table(x))
  colnames(tbl) <- c("Category", "Frequency")
  return(tbl)
})

combined_freq_df <- bind_rows(lapply(names(frequency_tables), function(name) {
  df <- frequency_tables[[name]]
  df$Variable <- name
  return(df)
}))

combined_freq_df <- combined_freq_df %>%
  pivot_wider(names_from = Variable, values_from = Frequency, values_fill = list(Frequency = 0)) %>%
  arrange(Category)

combined_freq_df <- combined_freq_df %>%
  dplyr::select(Category, everything())

combined_freq_df$Category <- as.character(combined_freq_df$Category)

total_row <- colSums(combined_freq_df[,-1])
total_row <- c("Total", total_row)

combined_freq_df <- rbind(combined_freq_df, total_row)

fancy_table2 <- flextable(combined_freq_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")

fancy_table2 <- set_caption(fancy_table2, caption = "Frequency tables for categorical endpoints after exlcuding NCSU (N=173)")

fancy_table2 <- fontsize(fancy_table2, size = 8, part = "body") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header
fancy_table2 <- border(fancy_table2, i = (nrow(fancy_table2$body$dataset)-1), border.bottom = fp_border(color = "black"))
fancy_table2 <- align(fancy_table2, align = "center", part = "body")

print(fancy_table2)
## a flextable object.
## col_keys: `Category`, `lowest cut ast`, `highest cut ast`, `lowest cut alt`, `highest cut alt`, `cirrhosis`, `cirrhosis diagnosed` 
## header has 1 row(s) 
## body has 3 row(s) 
## original dataset sample: 
##   Category lowest cut ast highest cut ast lowest cut alt highest cut alt
## 1 Abnormal             88              43             10               5
## 2   Normal             85             130            163             168
## 3    Total            173             173            173             173
##   cirrhosis cirrhosis diagnosed
## 1       170                   2
## 2         3                 145
## 3       173                 147
# Create a new Word document
doc <- read_docx()

# Add the first flextable
doc <- body_add_flextable(doc, value = fancy_table1)

# Add a page break (optional) or additional spacing
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")

# Add the second flextable
doc <- body_add_flextable(doc, value = fancy_table2)

# Save the document
print(doc, target = "Frequency tables of categorical outcome vars.docx")

# 7. Association between cat endpoints 
# 7-1 through correlation matrix
# 7-1-1. total ppl
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>% 
     cor(use="pairwise.complete.obs")



# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix, 
           show.diag=FALSE, 
           type="lower", 
           lab=TRUE, 
           lab_size=2,
           title="Correlation matrix of categorical outcomes in total ppl (N=349)") +  # Title of the plot
        theme(
          plot.title = element_text(size = 10), # Title font size
          axis.text.x = element_text(size = 8), # X-axis title font size
          axis.text.y = element_text(size = 8)  # Y-axis title font size
        )

print(plot6)

# 7-1-2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

# Relabel the 'cirrhosis' variables
data_renamed$cirrhosis <- factor(data_renamed$cirrhosis,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`,
                                 levels = c("Healthy", "Cirrhosis"),
                                 labels = c("Normal", "Abnormal"))

# Create dummy variables and compute correlation matrix
correlation_matrix <- model.matrix(~0 + ., data=data_renamed) %>% 
     cor(use="pairwise.complete.obs")


# Plot the correlation matrix using ggcorrplot
plot6 <-ggcorrplot(correlation_matrix, 
           show.diag=FALSE, 
           type="lower", 
           lab=TRUE, 
           lab_size=2,
           title="Correlation matrix of categorical outcomes after excluding NCSU data (N=173)") +  # Title of the plot
        theme(
          plot.title = element_text(size = 10), # Title font size
          axis.text.x = element_text(size = 8), # X-axis title font size
          axis.text.y = element_text(size = 8)  # Y-axis title font size
        )

print(plot6)

###########Tabulate

# 1. total ppl
# Create dummy variables
data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")

# Function to calculate p-values for correlations
cor.mtest <- function(mat, ...) {
  mat <- as.matrix(mat)
  n <- ncol(mat)
  p.mat <- matrix(NA, n, n)
  diag(p.mat) <- 0
  for (i in 1:(n - 1)) {
    for (j in (i + 1):n) {
      tmp <- cor.test(mat[, i], mat[, j], ...)
      p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
    }
  }
  colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
  p.mat
}

# Calculate p-values
p.mat <- cor.mtest(model_matrix)

# Convert correlation matrix and p-value matrix to long format
cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)

# Rename columns for merging
colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")

# Merge correlation and p-value data frames
plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))

# Check the structure and content of the merged data frame
print("Structure of plot_df:")
## [1] "Structure of plot_df:"
str(plot_df)
## 'data.frame':    49 obs. of  4 variables:
##  $ Var1       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
##  $ Var2       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
##  $ correlation: num  1 0.0582 0.3336 0.1052 -0.2435 ...
##  $ pvalue     : num  0 0.30221339661 0.00000000118 0.06181134671 0.000011963 ...
print("First few rows of plot_df:")
## [1] "First few rows of plot_df:"
head(plot_df)
print(significant_results)
## # A tibble: 98 × 6
##    pfas    term                             estimate std.error statistic p.value
##    <chr>   <chr>                               <dbl>     <dbl>     <dbl>   <dbl>
##  1 pf_hx_s age_at_enrollment                 0.0363    0.00857      4.24 2.89e-5
##  2 pf_hx_s smokingUnknown/Not Reported      -0.362     0.183       -1.97 4.91e-2
##  3 pf_hx_s sq_water_bottledUnknown/Not Rep… -0.771     0.219       -3.52 4.91e-4
##  4 pf_hx_s sq_water_bottledYes              -0.671     0.212       -3.17 1.65e-3
##  5 pfda    age_at_enrollment                 0.00451   0.00178      2.54 1.15e-2
##  6 pfda    smokingSmoke or use vape         -0.154     0.0576      -2.67 7.96e-3
##  7 pfda    smokingUnknown/Not Reported      -0.0864    0.0372      -2.32 2.07e-2
##  8 pfda    sq_drink_alcoholYes, current dr…  0.177     0.0469       3.78 1.80e-4
##  9 pfda    sq_water_bottledUnknown/Not Rep… -0.109     0.0452      -2.41 1.62e-2
## 10 pfda    sq_water_bottledYes              -0.105     0.0436      -2.41 1.65e-2
## # ℹ 88 more rows
# Create a formatted table using gt
significant_results %>%
  gt() %>%
  tab_header(
    title = "Significant Associations between PFAS and potential confounders From Simple LR"
  ) %>%
  cols_label(
    pfas = "PFAS",
    term = "Covariate",
    estimate = "Estimate",
    std.error = "Standard Error",
    statistic = "Statistic",
    p.value = "P-value"
  ) %>%
  fmt_number(
    columns = c(estimate, std.error, statistic, p.value),
    decimals = 3
  ) %>%
  tab_source_note(
    source_note = "Significant results with p-value < 0.05"
  )
Significant Associations between PFAS and potential confounders From Simple LR
PFAS Covariate Estimate Standard Error Statistic P-value
pf_hx_s age_at_enrollment 0.036 0.009 4.237 0.000
pf_hx_s smokingUnknown/Not Reported −0.362 0.183 −1.974 0.049
pf_hx_s sq_water_bottledUnknown/Not Reported −0.771 0.219 −3.516 0.000
pf_hx_s sq_water_bottledYes −0.671 0.212 −3.169 0.002
pfda age_at_enrollment 0.005 0.002 2.540 0.012
pfda smokingSmoke or use vape −0.154 0.058 −2.668 0.008
pfda smokingUnknown/Not Reported −0.086 0.037 −2.324 0.021
pfda sq_drink_alcoholYes, current drinker 0.177 0.047 3.784 0.000
pfda sq_water_bottledUnknown/Not Reported −0.109 0.045 −2.415 0.016
pfda sq_water_bottledYes −0.105 0.044 −2.408 0.017
pfna age_at_enrollment 0.012 0.003 4.197 0.000
pfna smokingSmoke or use vape −0.294 0.096 −3.079 0.002
pfna smokingUnknown/Not Reported −0.130 0.062 −2.107 0.036
pfna sq_drink_alcoholYes, current drinker 0.292 0.078 3.757 0.000
pfna sq_water_bottledUnknown/Not Reported −0.169 0.075 −2.253 0.025
pfos sourceUNC −3.388 1.678 −2.020 0.044
pfos age_at_enrollment 0.196 0.035 5.619 0.000
pfos race_final_labelMore than one race 17.960 7.933 2.264 0.024
pfos smokingSmoke or use vape −3.693 1.163 −3.175 0.002
pfos sq_drink_alcoholYes, current drinker 2.235 0.961 2.326 0.021
pfos sq_water_bottledUnknown/Not Reported −2.434 0.909 −2.677 0.008
pfos sq_water_bottledYes −2.587 0.878 −2.948 0.003
pf_hp_a sourceNCSU −0.019 0.007 −2.768 0.006
pf_hp_a race_final_labelAmerican Indian/Alaskan Native −0.198 0.044 −4.557 0.000
pf_hp_a race_final_labelAsian −0.170 0.046 −3.703 0.000
pf_hp_a race_final_labelAsian/Pacific Islander −0.169 0.041 −4.124 0.000
pf_hp_a race_final_labelBlack −0.187 0.036 −5.202 0.000
pf_hp_a race_final_labelMore than one race −0.151 0.062 −2.451 0.015
pf_hp_a race_final_labelOther −0.187 0.038 −4.879 0.000
pf_hp_a race_final_labelUnknown/Not Reported −0.193 0.040 −4.779 0.000
pf_hp_a race_final_labelWhite −0.159 0.036 −4.442 0.000
pfbs ruralLiving in rural area 0.042 0.019 2.172 0.030
pfbs sq_average_drink_per_day3-4 alcoholic drinks per day 0.150 0.049 3.076 0.002
pfoa age_at_enrollment 0.029 0.008 3.628 0.000
pfoa smokingSmoke or use vape −0.601 0.264 −2.279 0.023
pfoa sq_drink_alcoholYes, current drinker 0.764 0.214 3.573 0.000
pfoa sq_water_bottledUnknown/Not Reported −0.641 0.204 −3.140 0.002
pfoa sq_water_bottledYes −0.674 0.197 −3.420 0.001
pf_pe_a sourceEmory 0.019 0.005 4.093 0.000
pf_pe_a sourceNCSU 0.011 0.004 2.607 0.009
pf_pe_a sourceUNC 0.030 0.008 3.662 0.000
pf_pe_a sq_water_house_filtrationYes 0.016 0.006 2.742 0.006
pf_un_a sourceNCSU 0.041 0.018 2.246 0.025
pf_un_a sourceUNC −0.084 0.036 −2.309 0.021
pf_un_a age_at_enrollment 0.002 0.001 2.654 0.008
pf_un_a race_eth_labelNHO 0.107 0.049 2.174 0.030
pf_un_a race_final_labelAsian/Pacific Islander 0.275 0.115 2.396 0.017
pf_un_a smokingSmoke or use vape −0.080 0.025 −3.141 0.002
pf_un_a smokingUnknown/Not Reported −0.046 0.016 −2.786 0.006
pf_un_a sq_drink_alcoholYes, current drinker 0.074 0.021 3.557 0.000
pf_un_a sq_average_drink_per_dayUnknown/Not Reported −0.085 0.034 −2.489 0.013
pf_un_a sq_self_hep_bUnknown/Not Reported −0.032 0.016 −2.017 0.044
pf_un_a sq_self_hep_cUnknown/Not Reported −0.033 0.016 −2.040 0.042
pf_un_a sq_water_bottledUnknown/Not Reported −0.050 0.020 −2.500 0.013
pf_un_a sq_water_bottledYes −0.040 0.019 −2.076 0.039
pf_hp_s sourceUNC −0.089 0.044 −2.017 0.044
pf_hp_s age_at_enrollment 0.006 0.001 7.001 0.000
pf_hp_s sexMale 0.052 0.018 2.844 0.005
pf_hp_s smokingSmoke or use vape −0.102 0.031 −3.340 0.001
pf_hp_s sq_water_bottledUnknown/Not Reported −0.059 0.024 −2.458 0.014
pf_hp_s sq_water_bottledYes −0.074 0.023 −3.212 0.001
pf_do_a sourceUNC −0.017 0.009 −1.994 0.047
pf_do_a sq_drink_alcoholYes, current drinker 0.012 0.005 2.338 0.020
pf_do_a sq_water_bottledYes −0.009 0.005 −2.025 0.044
pf_pe_s sourceNCSU 0.008 0.004 2.077 0.038
pf_pe_s race_final_labelAmerican Indian/Alaskan Native −0.062 0.026 −2.375 0.018
pf_pe_s race_final_labelAsian/Pacific Islander −0.055 0.025 −2.217 0.027
pf_pe_s race_final_labelBlack −0.051 0.022 −2.355 0.019
pf_pe_s race_final_labelOther −0.053 0.023 −2.281 0.023
pf_pe_s race_final_labelUnknown/Not Reported −0.053 0.024 −2.180 0.030
pf_pe_s sq_average_drink_per_dayUnknown/Not Reported −0.016 0.007 −2.200 0.028
pf_pe_s sq_water_tap_unfilteredYes 0.008 0.004 1.996 0.047
pf_pe_s sq_water_bottledUnknown/Not Reported −0.009 0.004 −2.186 0.029
pf_pe_s sq_water_bottledYes −0.016 0.004 −3.935 0.000
pf_hx_a sourceEmory −0.005 0.001 −3.606 0.000
pf_hx_a sourceUNC 0.009 0.003 3.406 0.001
pf_hx_a smokingSmoke or use vape 0.004 0.002 2.158 0.032
pf_hx_a smokingUnknown/Not Reported 0.004 0.001 3.698 0.000
pf_hx_a sq_drink_alcoholUnknown/Not Reported 0.004 0.002 2.738 0.006
pf_hx_a sq_self_hep_bUnknown/Not Reported 0.003 0.001 3.020 0.003
pf_hx_a sq_self_hep_cUnknown/Not Reported 0.004 0.001 3.441 0.001
pf_hx_a sq_water_wellUnknown/Not Reported 0.003 0.001 2.558 0.011
pf_hx_a sq_water_tap_unfilteredUnknown/Not Reported 0.004 0.001 2.680 0.008
pf_hx_a sq_water_house_filtrationUnknown/Not Reported 0.003 0.001 2.555 0.011
pf_hx_a sq_water_faucet_filterUnknown/Not Reported 0.003 0.001 2.757 0.006
pf_hx_a sq_water_charcoal_filterUnknown/Not Reported 0.003 0.001 2.622 0.009
pf_hx_a sq_water_bottledUnknown/Not Reported 0.003 0.001 2.349 0.019
pf_hx_a sq_water_noneUnknown/Not Reported 0.003 0.001 2.639 0.009
pf_hx_a sq_water_other_typeUnknown/Not Reported 0.003 0.001 3.163 0.002
pf_hx_a sq_water_other_typeYes 0.006 0.002 2.808 0.005
pfba sourceEmory 0.032 0.009 3.516 0.000
pfba sexMale 0.020 0.007 2.994 0.003
pfba race_final_labelBlack −0.092 0.045 −2.037 0.042
pfba race_final_labelOther −0.099 0.048 −2.053 0.041
pfba sq_drink_alcoholNo, never drinker −0.021 0.010 −2.105 0.036
pfba sq_drink_alcoholUnknown/Not Reported −0.019 0.009 −2.050 0.041
pfba sq_drink_alcoholYes, current drinker −0.025 0.009 −2.677 0.008
pfba sq_water_faucet_filterYes 0.027 0.008 3.336 0.001
Significant results with p-value < 0.05
# Remove duplicate comparisons (keep only one direction)
plot_df <- plot_df %>%
  mutate(Var1 = as.character(Var1), Var2 = as.character(Var2)) %>%
  arrange(Var1, Var2) %>%
  distinct(Var1, Var2, .keep_all = TRUE) %>%
  filter(Var1 < Var2)  # Keep only one direction of the comparison

# Ensure pvalue is numeric for formatting
plot_df <- plot_df %>%
  mutate(pvalue = as.numeric(pvalue)) %>%
  mutate(pvalue = ifelse(pvalue < 0.001, "<0.001", sprintf("%.3f", pvalue)),
         correlation = sprintf("%.3f", correlation))

# Check the updated plot_df
print("Structure of updated plot_df:")
## [1] "Structure of updated plot_df:"
str(plot_df)
## 'data.frame':    21 obs. of  4 variables:
##  $ Var1       : chr  "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" ...
##  $ Var2       : chr  "`highest cut alt`Normal" "`highest cut ast`Normal" "`lowest cut alt`Normal" "`lowest cut ast`Abnormal" ...
##  $ correlation: chr  "0.058" "0.334" "0.105" "-0.244" ...
##  $ pvalue     : chr  "0.302" "<0.001" "0.062" "<0.001" ...
print("First few rows of updated plot_df:")
## [1] "First few rows of updated plot_df:"
head(plot_df)
ft <- flextable(plot_df)

ft <- set_table_properties(ft, width = 0.75, layout = "autofit") %>%
      set_header_labels(values = c(Var1 = "Cat Endpoint 1", Var2 = "Cat Endpoint 2", correlation = "Correlation", pvalue = "P")) %>%
      color(color = "#000000") %>%
      fontsize(size = 9) 
# Add caption 
ft <- set_caption(ft, caption = "Inter-correlation table of categorical endpoints in total ppl (N=349)")

print(ft)     
## a flextable object.
## col_keys: `Var1`, `Var2`, `correlation`, `pvalue` 
## header has 1 row(s) 
## body has 21 row(s) 
## original dataset sample: 
##                           Var1                     Var2 correlation pvalue
## 1 `cirrhosis diagnosed`Healthy  `highest cut alt`Normal       0.058  0.302
## 2 `cirrhosis diagnosed`Healthy  `highest cut ast`Normal       0.334 <0.001
## 3 `cirrhosis diagnosed`Healthy   `lowest cut alt`Normal       0.105  0.062
## 4 `cirrhosis diagnosed`Healthy `lowest cut ast`Abnormal      -0.244 <0.001
## 5 `cirrhosis diagnosed`Healthy   `lowest cut ast`Normal       0.244 <0.001
# 2. after excluding NCSU data
data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1','alt_cat2', 'cirrhosis','case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis','cirrhosis diagnosed')

model_matrix <- model.matrix(~0 + ., data = data_renamed)
correlation_matrix <- cor(model_matrix, use = "pairwise.complete.obs")

cor.mtest <- function(mat, ...) {
  mat <- as.matrix(mat)
  n <- ncol(mat)
  p.mat <- matrix(NA, n, n)
  diag(p.mat) <- 0
  for (i in 1:(n - 1)) {
    for (j in (i + 1):n) {
      tmp <- cor.test(mat[, i], mat[, j], ...)
      p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
    }
  }
  colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
  p.mat
}

p.mat <- cor.mtest(model_matrix)

cor_long <- melt(correlation_matrix)
pval_long <- melt(p.mat)

colnames(cor_long) <- c("Var1", "Var2", "correlation")
colnames(pval_long) <- c("Var1", "Var2", "pvalue")

plot_df <- merge(cor_long, pval_long, by = c("Var1", "Var2"))

print("Structure of plot_df:")
## [1] "Structure of plot_df:"
str(plot_df)
## 'data.frame':    49 obs. of  4 variables:
##  $ Var1       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 7 7 7 7 7 7 5 5 5 ...
##  $ Var2       : Factor w/ 7 levels "`lowest cut ast`Abnormal",..: 7 5 3 4 1 2 6 7 5 3 ...
##  $ correlation: num  1 -0.0232 0.0695 -0.0296 -0.1221 ...
##  $ pvalue     : num  0 0.786 0.414 0.728 0.151 ...
print("First few rows of plot_df:")
## [1] "First few rows of plot_df:"
head(plot_df)
plot_df <- plot_df %>%
  mutate(Var1 = as.character(Var1), Var2 = as.character(Var2)) %>%
  arrange(Var1, Var2) %>%
  distinct(Var1, Var2, .keep_all = TRUE) %>%
  filter(Var1 < Var2)  # Keep only one direction of the comparison

# Ensure pvalue is numeric for formatting
plot_df <- plot_df %>%
  mutate(pvalue = as.numeric(pvalue)) %>%
  mutate(pvalue = ifelse(pvalue < 0.001, "<0.001", sprintf("%.3f", pvalue)),
         correlation = sprintf("%.3f", correlation))

print("Structure of updated plot_df:")
## [1] "Structure of updated plot_df:"
str(plot_df)
## 'data.frame':    21 obs. of  4 variables:
##  $ Var1       : chr  "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" "`cirrhosis diagnosed`Healthy" ...
##  $ Var2       : chr  "`highest cut alt`Normal" "`highest cut ast`Normal" "`lowest cut alt`Normal" "`lowest cut ast`Abnormal" ...
##  $ correlation: chr  "-0.023" "0.070" "-0.030" "-0.122" ...
##  $ pvalue     : chr  "0.786" "0.414" "0.728" "0.151" ...
print("First few rows of updated plot_df:")
## [1] "First few rows of updated plot_df:"
head(plot_df)
ft1 <- flextable(plot_df)

ft1 <- set_table_properties(ft1, width = 0.75, layout = "autofit") %>%
      set_header_labels(values = c(Var1 = "Cat Endpoint 1", Var2 = "Cat Endpoint 2", correlation = "Correlation", pvalue = "P")) %>%
      color(color = "#000000") %>%
      fontsize(size = 9) 
# Add caption 
ft1 <- set_caption(ft1, caption = "Inter-correlation table of categorical endpoints after excluding NCSU data (N=173)")

print(ft1)     
## a flextable object.
## col_keys: `Var1`, `Var2`, `correlation`, `pvalue` 
## header has 1 row(s) 
## body has 21 row(s) 
## original dataset sample: 
##                           Var1                     Var2 correlation pvalue
## 1 `cirrhosis diagnosed`Healthy  `highest cut alt`Normal      -0.023  0.786
## 2 `cirrhosis diagnosed`Healthy  `highest cut ast`Normal       0.070  0.414
## 3 `cirrhosis diagnosed`Healthy   `lowest cut alt`Normal      -0.030  0.728
## 4 `cirrhosis diagnosed`Healthy `lowest cut ast`Abnormal      -0.122  0.151
## 5 `cirrhosis diagnosed`Healthy   `lowest cut ast`Normal       0.122  0.151
#save
doc <- read_docx()
doc <- body_add_flextable(doc, ft)

doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, ft1)

print(doc, target = "Correlation of intercategorical endpoints.docx")

Diagnosis of categorical endpoints

#1. endpoint diagnosis
results <- list()

data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))

variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

for (i in seq_along(variables)) {
  for (j in seq_along(variables)) {
    if (i != j) {
      # Ensure both variables have the same length and no NAs
      if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
        
        # Remove rows with NA values in either variable
        valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
        valid_data_i <- data_renamed[[variables[i]]][valid_indices]
        valid_data_j <- data_renamed[[variables[j]]][valid_indices]
        
        # Print data for debugging
        print(paste("Data for", variables[i], "vs", variables[j]))
        print(head(valid_data_i))
        print(head(valid_data_j))
        
        # Generate confusion matrix
        conf_matrix <- table(predicted = valid_data_i, actual = valid_data_j)
        
        # Print confusion matrix for debugging
        print(paste("Confusion Matrix for", variables[i], "vs", variables[j]))
        print(conf_matrix)
        
        # Get levels from confusion matrix
        levels_i <- rownames(conf_matrix)
        levels_j <- colnames(conf_matrix)
        
        # Initialize metric values
        sensitivity <- specificity <- ppv <- npv <- accuracy <- auc_value <- f1_score <- kappa_value <- NA
        
        # Check if both 'Abnormal/cirrhosis' and 'Normal' are present in confusion matrix
        if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
            "Normal" %in% levels_i && "Normal" %in% levels_j) {
          
          # Extract values safely
          true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
          false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
          
          # Calculate metrics
          sensitivity <- true_pos / (true_pos + false_neg)
          specificity <- true_neg / (true_neg + false_pos)
          ppv <- true_pos / (true_pos + false_pos)
          npv <- true_neg / (true_neg + false_neg)
          accuracy <- (true_pos + true_neg) / sum(conf_matrix)
          
          # Ensure valid_data_i is numeric and valid_data_j is a factor
          valid_data_i_numeric <- as.numeric(factor(valid_data_i, levels = unique(valid_data_i)))
          valid_data_j_factor <- factor(valid_data_j, levels = c("Normal", "Abnormal")) # Assuming "High" is positive class
          
          # Print data for ROC debugging
          print("Valid Data for ROC")
          print(head(valid_data_i_numeric))
          print(head(valid_data_j_factor))
          
          # ROC Curve and AUC
          roc_curve <- tryCatch({
            roc(valid_data_j_factor, valid_data_i_numeric)
          }, error = function(e) {
            print("Error in ROC calculation")
            return(NULL)
          })
          
          auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
          
          # F1 Score
          f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
            2 * (ppv * sensitivity) / (ppv + sensitivity)
          } else {
            NA
          }
          
          # Kappa Statistic
          kappa_value <- tryCatch({
            kappa2(conf_matrix)$value
          }, error = function(e) {
            print("Error in Kappa calculation")
            return(NA)
          })
        }
        
        # Store results
        results[[paste(variables[i], "vs", variables[j])]] <- c(
          Sensitivity = sensitivity,
          Specificity = specificity,
          PPV = ppv,
          NPV = npv,
          Accuracy = accuracy,
          AUC = auc_value,
          F1_Score = f1_score,
          Kappa = kappa_value
        )
      } else {
        results[[paste(variables[i], "vs", variables[j])]] <- c(
          Sensitivity = NA,
          Specificity = NA,
          PPV = NA,
          NPV = NA,
          Accuracy = NA,
          AUC = NA,
          F1_Score = NA,
          Kappa = NA
        )
      }
    }
  }
}
## [1] "Data for lowest cut ast vs highest cut ast"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Confusion Matrix for lowest cut ast vs highest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal      165     57
##   Normal          0    127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal   Normal   Normal   Normal   Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut ast vs lowest cut alt"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut ast vs lowest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       49    173
##   Normal          0    127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut ast vs highest cut alt"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut ast vs highest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28    194
##   Normal          0    127
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut ast vs cirrhosis"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis"
##           actual
## predicted  Abnormal Normal
##   Abnormal      215      7
##   Normal        126      1
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut ast vs cirrhosis diagnosed"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      101    102
##   Normal         28     85
## [1] "Valid Data for ROC"
## [1] 1 2 1 2 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut ast vs lowest cut ast"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Confusion Matrix for highest cut ast vs lowest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal      165      0
##   Normal         57    127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal   Abnormal Normal   Abnormal Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut ast vs lowest cut alt"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut ast vs lowest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       49    116
##   Normal          0    184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut ast vs highest cut alt"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut ast vs highest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28    137
##   Normal          0    184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut ast vs cirrhosis"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut ast vs cirrhosis"
##           actual
## predicted  Abnormal Normal
##   Abnormal      158      7
##   Normal        183      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls > cases
## [1] "Data for highest cut ast vs cirrhosis diagnosed"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       90     67
##   Normal         39    120
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 2
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut alt vs lowest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Confusion Matrix for lowest cut alt vs lowest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal       49      0
##   Normal        173    127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Abnormal Normal   Abnormal Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut alt vs highest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Confusion Matrix for lowest cut alt vs highest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal       49      0
##   Normal        116    184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Normal   Normal   Normal   Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut alt vs highest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for lowest cut alt vs highest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28     21
##   Normal          0    300
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for lowest cut alt vs cirrhosis"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis"
##           actual
## predicted  Abnormal Normal
##   Abnormal       42      7
##   Normal        299      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls > cases
## [1] "Data for lowest cut alt vs cirrhosis diagnosed"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       25     22
##   Normal        104    165
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut alt vs lowest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Confusion Matrix for highest cut alt vs lowest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28      0
##   Normal        194    127
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Abnormal Normal   Abnormal Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut alt vs highest cut ast"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Confusion Matrix for highest cut alt vs highest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28      0
##   Normal        137    184
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Normal   Normal   Normal   Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut alt vs lowest cut alt"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for highest cut alt vs lowest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       28      0
##   Normal         21    300
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for highest cut alt vs cirrhosis"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut alt vs cirrhosis"
##           actual
## predicted  Abnormal Normal
##   Abnormal       21      7
##   Normal        320      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls > cases
## [1] "Data for highest cut alt vs cirrhosis diagnosed"
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       14     14
##   Normal        115    173
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis vs lowest cut ast"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Confusion Matrix for cirrhosis vs lowest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal      215    126
##   Normal          7      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Abnormal Normal   Abnormal Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis vs highest cut ast"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Confusion Matrix for cirrhosis vs highest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal      158    183
##   Normal          7      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Normal   Normal   Normal   Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis vs lowest cut alt"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis vs lowest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       42    299
##   Normal          7      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis vs highest cut alt"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis vs highest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       21    320
##   Normal          7      1
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis vs cirrhosis diagnosed"
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      128    180
##   Normal          1      7
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis diagnosed vs lowest cut ast"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal"   "Abnormal" "Normal"   "Abnormal" "Normal"   "Abnormal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs lowest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal      101     28
##   Normal        102     85
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Abnormal Normal   Abnormal Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis diagnosed vs highest cut ast"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal"   "Normal"   "Normal"   "Normal"   "Normal"   "Abnormal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs highest cut ast"
##           actual
## predicted  Abnormal Normal
##   Abnormal       90     39
##   Normal         67    120
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal   Normal   Normal   Normal   Normal   Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis diagnosed vs lowest cut alt"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs lowest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       25    104
##   Normal         22    165
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis diagnosed vs highest cut alt"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] "Normal" "Normal" "Normal" "Normal" "Normal" "Normal"
## [1] "Confusion Matrix for cirrhosis diagnosed vs highest cut alt"
##           actual
## predicted  Abnormal Normal
##   Abnormal       14    115
##   Normal         14    173
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Data for cirrhosis diagnosed vs cirrhosis"
## [1] Normal Normal Normal Normal Normal Normal
## Levels: Abnormal Normal
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Abnormal Normal
## [1] "Confusion Matrix for cirrhosis diagnosed vs cirrhosis"
##           actual
## predicted  Abnormal Normal
##   Abnormal      128      1
##   Normal        180      7
## [1] "Valid Data for ROC"
## [1] 1 1 1 1 1 1
## [1] Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal
## Levels: Normal Abnormal
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)

results_df <- results_df %>% dplyr::select(-AUC)

results_df <- results_df %>%
  mutate(across(everything(), ~ case_when(
    . == 1.000 ~ "1",
    . == 0.000 ~ "0",
    TRUE ~ sprintf("%.3f", .)
  )))


results_df$Comparison <- rownames(results_df)

results_df <- results_df %>% dplyr::select(Comparison, everything())

fancy_table <- flextable(results_df)
fancy_table <- set_table_properties(fancy_table, width = 0.8, layout = "autofit")

fancy_table <- set_caption(fancy_table, caption = "Confusion matrix results table (N=349)")

fancy_table <- fontsize(fancy_table, size = 8, part = "all") # Reduce font size of values
fancy_table <- fontsize(fancy_table, size = 9, part = "header") # Reduce font size of header

print(fancy_table)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 30 row(s) 
## original dataset sample: 
##                                                                  Comparison
## lowest cut ast vs highest cut ast         lowest cut ast vs highest cut ast
## lowest cut ast vs lowest cut alt           lowest cut ast vs lowest cut alt
## lowest cut ast vs highest cut alt         lowest cut ast vs highest cut alt
## lowest cut ast vs cirrhosis                     lowest cut ast vs cirrhosis
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
##                                       Sensitivity Specificity   PPV   NPV
## lowest cut ast vs highest cut ast           0.743           1     1 0.690
## lowest cut ast vs lowest cut alt            0.221           1     1 0.423
## lowest cut ast vs highest cut alt           0.126           1     1 0.396
## lowest cut ast vs cirrhosis                 0.968       0.008 0.630 0.125
## lowest cut ast vs cirrhosis diagnosed       0.498       0.752 0.783 0.455
##                                       Accuracy F1_Score Kappa
## lowest cut ast vs highest cut ast        0.837    0.853     0
## lowest cut ast vs lowest cut alt         0.504    0.362     0
## lowest cut ast vs highest cut alt        0.444    0.224     0
## lowest cut ast vs cirrhosis              0.619    0.764     0
## lowest cut ast vs cirrhosis diagnosed    0.589    0.608     0
#2. setting case_control as gold standard in total ppl
results <- list()
seen_comparisons <- character()  # Initialize seen_comparisons as a character vector

data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))

variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'

for (i in seq_along(variables)) {
  for (j in seq_along(variables)) {
    if (i != j) {
      # Define the comparison names
      comparison_name_1 <- paste(variables[i], "vs", variables[j])
      comparison_name_2 <- paste(variables[j], "vs", variables[i])
      
      # Skip if the reverse comparison has already been processed
      if (comparison_name_2 %in% seen_comparisons) {
        next
      }
      
      # Mark the first comparison as seen
      seen_comparisons <- c(seen_comparisons, comparison_name_1)
      
      # Ensure both variables have the same length and no NAs
      if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
        
        # Remove rows with NA values in either variable
        valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
        valid_data_i <- data_renamed[[variables[i]]][valid_indices]
        valid_data_j <- data_renamed[[variables[j]]][valid_indices]
        
        # Check if gold standard is involved
        if (variables[j] == gold_standard) {
          predicted <- valid_data_i
          actual <- valid_data_j
        } else if (variables[i] == gold_standard) {
          predicted <- valid_data_j
          actual <- valid_data_i
        } else {
          next
        }
        
        # Generate confusion matrix
        conf_matrix <- table(predicted = predicted, actual = actual)
        
        # Print confusion matrix for debugging
        print(paste("Confusion Matrix for", comparison_name_1))
        print(conf_matrix)
        
        # Check for at least one Abnormal and one Normal class in both predicted and actual
        levels_i <- rownames(conf_matrix)
        levels_j <- colnames(conf_matrix)
        
        if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
            "Normal" %in% levels_i && "Normal" %in% levels_j) {
          
          # Extract values safely
          true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
          false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
          
          # Calculate metrics
          sensitivity <- true_pos / (true_pos + false_neg)
          specificity <- true_neg / (true_neg + false_pos)
          ppv <- true_pos / (true_pos + false_pos)
          npv <- true_neg / (true_neg + false_neg)
          accuracy <- (true_pos + true_neg) / sum(conf_matrix)
          
          # Ensure predicted is numeric and actual is a factor
          predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
          actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
          
          # ROC Curve and AUC
          roc_curve <- tryCatch({
            roc(actual_factor, predicted_numeric)
          }, error = function(e) {
            print("Error in ROC calculation")
            return(NULL)
          })
          
          auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
          
          # F1 Score
          f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
            2 * (ppv * sensitivity) / (ppv + sensitivity)
          } else {
            NA
          }
          
          # Kappa Statistic
          kappa_value <- tryCatch({
            kappa2(conf_matrix)$value
          }, error = function(e) {
            print("Error in Kappa calculation")
            return(NA)
          })
          
          # Store results
          results[[comparison_name_1]] <- c(
            Sensitivity = sensitivity,
            Specificity = specificity,
            PPV = ppv,
            NPV = npv,
            Accuracy = accuracy,
            AUC = auc_value,
            F1_Score = f1_score,
            Kappa = kappa_value
          )
        } else {
          results[[comparison_name_1]] <- c(
            Sensitivity = NA,
            Specificity = NA,
            PPV = NA,
            NPV = NA,
            Accuracy = NA,
            AUC = NA,
            F1_Score = NA,
            Kappa = NA
          )
        }
      } else {
        results[[comparison_name_1]] <- c(
          Sensitivity = NA,
          Specificity = NA,
          PPV = NA,
          NPV = NA,
          Accuracy = NA,
          AUC = NA,
          F1_Score = NA,
          Kappa = NA
        )
      }
    }
  }
}
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      101    102
##   Normal         28     85
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       90     67
##   Normal         39    120
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       25     22
##   Normal        104    165
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       14     14
##   Normal        115    173
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      128    180
##   Normal          1      7
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)

# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)

# Format results
results_df <- results_df %>%
  mutate(across(everything(), ~ case_when(
    . == 1.000 ~ "1",
    . == 0.000 ~ "0",
    TRUE ~ sprintf("%.3f", .)
  )))

results_df$Comparison <- rownames(results_df)

# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())

# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
  filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))

# Create flextable
fancy_table1 <- flextable(results_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")

fancy_table1 <- set_caption(fancy_table1, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) in total ppl (N=349)")

fancy_table1 <- fontsize(fancy_table1, size = 8, part = "all") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header

# Print flextable
print(fancy_table1)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 5 row(s) 
## original dataset sample: 
##                                                                    Comparison
## lowest cut ast vs cirrhosis diagnosed   lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed   lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed             cirrhosis vs cirrhosis diagnosed
##                                        Sensitivity Specificity   PPV   NPV
## lowest cut ast vs cirrhosis diagnosed        0.498       0.752 0.783 0.455
## highest cut ast vs cirrhosis diagnosed       0.573       0.755 0.698 0.642
## lowest cut alt vs cirrhosis diagnosed        0.532       0.613 0.194 0.882
## highest cut alt vs cirrhosis diagnosed       0.500       0.601 0.109 0.925
## cirrhosis vs cirrhosis diagnosed             0.416       0.875 0.992 0.037
##                                        Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed     0.589    0.608     0
## highest cut ast vs cirrhosis diagnosed    0.665    0.629     0
## lowest cut alt vs cirrhosis diagnosed     0.601    0.284     0
## highest cut alt vs cirrhosis diagnosed    0.592    0.178 0.333
## cirrhosis vs cirrhosis diagnosed          0.427    0.586     0
############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)

results_long <- results_df %>%
  pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")

results_long <- results_long %>%
  mutate(Value = as.numeric(Value)) %>%
  filter(!is.na(Value)) 

new_labels <- c(
  "cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
  "highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
  "lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
  "highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
  "lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)

palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")  

# Recode the Comparison column
results_long <- results_long %>%
  mutate(Comparison = recode(Comparison, !!!new_labels))


# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
  geom_point(size = 3) +
  labs(title = "Diagnostic testing in total",
       x = "Scores",
       y = "Measure of diagnostic test performance",
       color = "Comparison",
       shape = "Comparison") +
  scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
  theme(axis.text.y = element_text(size = 10),
        axis.text.x = element_text(size = 10),
        axis.title = element_text(size = 12),
        legend.position = "right") +
  scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
  scale_color_manual(values = palette_colors)

# 3. after excluding NCSU data
results <- list()
seen_comparisons <- character()  # Initialize seen_comparisons as a character vector

data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))

variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'

for (i in seq_along(variables)) {
  for (j in seq_along(variables)) {
    if (i != j) {
      # Define the comparison names
      comparison_name_1 <- paste(variables[i], "vs", variables[j])
      comparison_name_2 <- paste(variables[j], "vs", variables[i])
      
      # Skip if the reverse comparison has already been processed
      if (comparison_name_2 %in% seen_comparisons) {
        next
      }
      
      # Mark the first comparison as seen
      seen_comparisons <- c(seen_comparisons, comparison_name_1)
      
      # Ensure both variables have the same length and no NAs
      if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
        
        # Remove rows with NA values in either variable
        valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
        valid_data_i <- data_renamed[[variables[i]]][valid_indices]
        valid_data_j <- data_renamed[[variables[j]]][valid_indices]
        
        # Check if gold standard is involved
        if (variables[j] == gold_standard) {
          predicted <- valid_data_i
          actual <- valid_data_j
        } else if (variables[i] == gold_standard) {
          predicted <- valid_data_j
          actual <- valid_data_i
        } else {
          next
        }
        
        # Generate confusion matrix
        conf_matrix <- table(predicted = predicted, actual = actual)
        
        # Print confusion matrix for debugging
        print(paste("Confusion Matrix for", comparison_name_1))
        print(conf_matrix)
        
        # Check for at least one Abnormal and one Normal class in both predicted and actual
        levels_i <- rownames(conf_matrix)
        levels_j <- colnames(conf_matrix)
        
        if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
            "Normal" %in% levels_i && "Normal" %in% levels_j) {
          
          # Extract values safely
          true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
          false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
          
          # Calculate metrics
          sensitivity <- true_pos / (true_pos + false_neg)
          specificity <- true_neg / (true_neg + false_pos)
          ppv <- true_pos / (true_pos + false_pos)
          npv <- true_neg / (true_neg + false_neg)
          accuracy <- (true_pos + true_neg) / sum(conf_matrix)
          
          # Ensure predicted is numeric and actual is a factor
          predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
          actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
          
          # ROC Curve and AUC
          roc_curve <- tryCatch({
            roc(actual_factor, predicted_numeric)
          }, error = function(e) {
            print("Error in ROC calculation")
            return(NULL)
          })
          
          auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
          
          # F1 Score
          f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
            2 * (ppv * sensitivity) / (ppv + sensitivity)
          } else {
            NA
          }
          
          # Kappa Statistic
          kappa_value <- tryCatch({
            kappa2(conf_matrix)$value
          }, error = function(e) {
            print("Error in Kappa calculation")
            return(NA)
          })
          
          # Store results
          results[[comparison_name_1]] <- c(
            Sensitivity = sensitivity,
            Specificity = specificity,
            PPV = ppv,
            NPV = npv,
            Accuracy = accuracy,
            AUC = auc_value,
            F1_Score = f1_score,
            Kappa = kappa_value
          )
        } else {
          results[[comparison_name_1]] <- c(
            Sensitivity = NA,
            Specificity = NA,
            PPV = NA,
            NPV = NA,
            Accuracy = NA,
            AUC = NA,
            F1_Score = NA,
            Kappa = NA
          )
        }
      } else {
        results[[comparison_name_1]] <- c(
          Sensitivity = NA,
          Specificity = NA,
          PPV = NA,
          NPV = NA,
          Accuracy = NA,
          AUC = NA,
          F1_Score = NA,
          Kappa = NA
        )
      }
    }
  }
}
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        2     67
##   Normal          0     71
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        1     34
##   Normal          1    104
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        0      8
##   Normal          2    130
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        0      5
##   Normal          2    133
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        2    135
##   Normal          0      3
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)

# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)

# Format results
results_df <- results_df %>%
  mutate(across(everything(), ~ case_when(
    . == 1.000 ~ "1",
    . == 0.000 ~ "0",
    TRUE ~ sprintf("%.3f", .)
  )))

results_df$Comparison <- rownames(results_df)

# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())

# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
  filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))

# Create flextable
fancy_table2 <- flextable(results_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")

fancy_table2 <- set_caption(fancy_table2, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) after excluding NCSU (N=173)")

fancy_table2 <- fontsize(fancy_table2, size = 8, part = "all") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header

# Print flextable
print(fancy_table2)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 5 row(s) 
## original dataset sample: 
##                                                                    Comparison
## lowest cut ast vs cirrhosis diagnosed   lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed   lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed             cirrhosis vs cirrhosis diagnosed
##                                        Sensitivity Specificity   PPV   NPV
## lowest cut ast vs cirrhosis diagnosed        0.029           1     1 0.514
## highest cut ast vs cirrhosis diagnosed       0.029       0.990 0.500 0.754
## lowest cut alt vs cirrhosis diagnosed            0       0.985     0 0.942
## highest cut alt vs cirrhosis diagnosed           0       0.985     0 0.964
## cirrhosis vs cirrhosis diagnosed             0.015           1     1 0.022
##                                        Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed     0.521    0.056     0
## highest cut ast vs cirrhosis diagnosed    0.750    0.054     0
## lowest cut alt vs cirrhosis diagnosed     0.929       NA     0
## highest cut alt vs cirrhosis diagnosed    0.950       NA     0
## cirrhosis vs cirrhosis diagnosed          0.036    0.029     0
###
doc <- read_docx()
doc <- body_add_flextable(doc, fancy_table)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")

doc <- body_add_flextable(doc, fancy_table1)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table2)

print(doc, target = "Categorical endpoint diagnosis.docx")


############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)

results_long <- results_df %>%
  pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")

results_long <- results_long %>%
  mutate(Value = as.numeric(Value)) %>%
  filter(!is.na(Value)) 
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Value = as.numeric(Value)`.
## Caused by warning:
## ! NAs introduced by coercion
new_labels <- c(
  "cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
  "highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
  "lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
  "highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
  "lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)

palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")  

# Recode the Comparison column
results_long <- results_long %>%
  mutate(Comparison = recode(Comparison, !!!new_labels))



# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
  geom_point(size = 3) +
  labs(title = "Diagnostic testing after excluding NCSU",
       x = "Scores",
       y = "Measure of diagnostic test performance",
       color = "Comparison",
       shape = "Comparison") +
  scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
  theme(axis.text.y = element_text(size = 10),
        axis.text.x = element_text(size = 10),
        axis.title = element_text(size = 12),
        legend.position = "right") +
  scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
  scale_color_manual(values = palette_colors)

Correlation heat map btw PFAS and potential confounders

# reset up reference category of some potential confounders
vars_to_convert <- c("source", "race_final_label","sq_drink_alcohol","sq_average_drink_per_day")
data[vars_to_convert] <- lapply(data[vars_to_convert], as.factor)

data$source <- relevel(data$source, ref = "Emory")
data$race_final_label <- relevel(data$race_final_label, ref = "White")
data$sq_drink_alcohol <- relevel(data$sq_drink_alcohol, ref = "No, never drinker")
data$sq_average_drink_per_day <- relevel(data$sq_average_drink_per_day, ref = "Less than 1 alcoholic drink per day")

# Define continuous potential confounders
numeric_data <- data %>%
     dplyr::select(all_of(potential_conf)) %>%
     dplyr::select_if(is.numeric)

conti_conf <- names(numeric_data)

# Function to extract model summary for continuous covariates
extract_model_summary <- function(pfas, covariate, data) {
  model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
  summary_model <- summary(model)
  estimate <- coef(summary_model)[2, 1]
  p_value <- coef(summary_model)[2, 4]
  return(data.frame(Confounders = covariate, Coeff = estimate, P = p_value, Factor = covariate, PFAS = pfas))
}

# Apply to all PFAS variables and continuous covariates
continuous_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
  bind_rows(lapply(conti_conf, function(cov) extract_model_summary(pfas, cov, data)))
}))

# Print continuous results
print(continuous_results)
##          Confounders          Coeff                   P            Factor
## 1  age_at_enrollment  0.02303963456 0.00002886969906048 age_at_enrollment
## 2                bmi -0.01294812141 0.09611802745084295               bmi
## 3        trig_mg_d_l  0.00006026119 0.92894071463283046       trig_mg_d_l
## 4  age_at_enrollment  0.01401689114 0.01151280974826796 age_at_enrollment
## 5                bmi -0.01953451965 0.01049324625441954               bmi
## 6        trig_mg_d_l -0.00087134772 0.19560135743360868       trig_mg_d_l
## 7  age_at_enrollment  0.02285242310 0.00003418692654294 age_at_enrollment
## 8                bmi -0.01016268100 0.19162630103330469               bmi
## 9        trig_mg_d_l -0.00012960498 0.84543151201780353       trig_mg_d_l
## 10 age_at_enrollment  0.03015972790 0.00000003849922364 age_at_enrollment
## 11               bmi -0.00632410823 0.39797483121906918               bmi
## 12       trig_mg_d_l -0.00060423845 0.36564464944825859       trig_mg_d_l
## 13 age_at_enrollment  0.00610856935 0.27012846280853531 age_at_enrollment
## 14               bmi -0.00353210464 0.63621285405847949               bmi
## 15       trig_mg_d_l -0.00037474559 0.57810255513241993       trig_mg_d_l
## 16 age_at_enrollment -0.00039411970 0.94418956366096030 age_at_enrollment
## 17               bmi  0.00294487490 0.67943161804413910               bmi
## 18       trig_mg_d_l -0.00051069442 0.45289086346786955       trig_mg_d_l
## 19 age_at_enrollment  0.01990379953 0.00032706545799936 age_at_enrollment
## 20               bmi -0.01850220830 0.01730193122393180               bmi
## 21       trig_mg_d_l  0.00025406806 0.70465471480120978       trig_mg_d_l
## 22 age_at_enrollment  0.00422728729 0.41715088028540981 age_at_enrollment
## 23               bmi -0.00405085209 0.60753830998090086               bmi
## 24       trig_mg_d_l -0.00053365608 0.39127771559889879       trig_mg_d_l
## 25 age_at_enrollment  0.01474075004 0.00829630670610244 age_at_enrollment
## 26               bmi -0.02788390573 0.00016089041529066               bmi
## 27       trig_mg_d_l -0.00105386273 0.11863558257777872       trig_mg_d_l
## 28 age_at_enrollment  0.03647003290 0.00000000001254972 age_at_enrollment
## 29               bmi -0.00498536122 0.49783998070122160               bmi
## 30       trig_mg_d_l  0.00035106604 0.59487932766741591       trig_mg_d_l
## 31 age_at_enrollment  0.00666942172 0.23478386028671305 age_at_enrollment
## 32               bmi -0.02691555479 0.00053014713321087               bmi
## 33       trig_mg_d_l -0.00088518107 0.19110820055049224       trig_mg_d_l
## 34 age_at_enrollment  0.00723125430 0.18396989965273475 age_at_enrollment
## 35               bmi -0.02551872568 0.00081610677927414               bmi
## 36       trig_mg_d_l -0.00011850725 0.86073672780481414       trig_mg_d_l
## 37 age_at_enrollment -0.00767217344 0.16771220778328327 age_at_enrollment
## 38               bmi  0.00258183929 0.73804930867047069               bmi
## 39       trig_mg_d_l -0.00082944038 0.21370329383697809       trig_mg_d_l
## 40 age_at_enrollment  0.00156141004 0.75969057121585326 age_at_enrollment
## 41               bmi -0.00261010632 0.73748118274195495               bmi
## 42       trig_mg_d_l -0.00015137036 0.80919075133267548       trig_mg_d_l
##            PFAS
## 1  pf_hx_s_scld
## 2  pf_hx_s_scld
## 3  pf_hx_s_scld
## 4     pfda_scld
## 5     pfda_scld
## 6     pfda_scld
## 7     pfna_scld
## 8     pfna_scld
## 9     pfna_scld
## 10    pfos_scld
## 11    pfos_scld
## 12    pfos_scld
## 13 pf_hp_a_scld
## 14 pf_hp_a_scld
## 15 pf_hp_a_scld
## 16    pfbs_scld
## 17    pfbs_scld
## 18    pfbs_scld
## 19    pfoa_scld
## 20    pfoa_scld
## 21    pfoa_scld
## 22 pf_pe_a_scld
## 23 pf_pe_a_scld
## 24 pf_pe_a_scld
## 25 pf_un_a_scld
## 26 pf_un_a_scld
## 27 pf_un_a_scld
## 28 pf_hp_s_scld
## 29 pf_hp_s_scld
## 30 pf_hp_s_scld
## 31 pf_do_a_scld
## 32 pf_do_a_scld
## 33 pf_do_a_scld
## 34 pf_pe_s_scld
## 35 pf_pe_s_scld
## 36 pf_pe_s_scld
## 37 pf_hx_a_scld
## 38 pf_hx_a_scld
## 39 pf_hx_a_scld
## 40    pfba_scld
## 41    pfba_scld
## 42    pfba_scld
# Define categorical confounders
cate_conf <- setdiff(potential_conf, conti_conf)

data <- data %>%
  mutate(across(all_of(cate_conf), as.factor))

# Function to fit model and handle categorical variables
fit_model_cat <- function(pfas, covariate, data) {
  model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
  summary_model <- summary(model)
  coefficients_df <- as.data.frame(summary_model$coefficients)
  coefficients_df <- coefficients_df[-1, ]  # Exclude intercept

  results <- data.frame()

  for (i in 1:nrow(coefficients_df)) {
    current_result <- data.frame(
      Confounders = covariate, 
      Coeff = coefficients_df[i, "Estimate"], 
      P = coefficients_df[i, "Pr(>|t|)"],
      Factor = rownames(coefficients_df)[i],
      PFAS = pfas
    )
    results <- rbind(results, current_result)
  }
  
  return(results)
}

# Apply to all PFAS variables and categorical covariates
categorical_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
  bind_rows(lapply(cate_conf, function(cov) fit_model_cat(pfas, cov, data)))
}))

# Print categorical results
print(categorical_results)
##                   Confounders        Coeff                P
## 1                      source  0.200895798 0.17816637785406
## 2                      source  0.287731004 0.02406241741592
## 3                      source -0.240398916 0.34812012829126
## 4                         sex  0.187197691 0.08219582251875
## 5              race_eth_label  0.050788492 0.84233629638901
## 6              race_eth_label -0.115104272 0.74149107535182
## 7              race_eth_label  0.274229818 0.26292213105599
## 8              race_eth_label  0.038885809 0.89888254812528
## 9            race_final_label -0.125411971 0.86037830965616
## 10           race_final_label -0.567654328 0.26261635127197
## 11           race_final_label -0.094110264 0.87184652297268
## 12           race_final_label -0.407973324 0.32604479871633
## 13           race_final_label -0.209886202 0.07137795642001
## 14           race_final_label -0.264366169 0.79275654669035
## 15           race_final_label -0.293394272 0.32387441802348
## 16           race_final_label -0.140730470 0.71483775593415
## 17                  ethnicity  0.189177168 0.43536968718843
## 18                  ethnicity  0.038885809 0.89927233218110
## 19                      rural -0.246551067 0.18530426138296
## 20                      rural -0.147877097 0.36453370981274
## 21                    smoking -0.323810274 0.07305486987873
## 22                    smoking -0.229485806 0.04908826819920
## 23           sq_drink_alcohol -0.091942208 0.55776878080577
## 24           sq_drink_alcohol -0.224849555 0.12238274703819
## 25           sq_drink_alcohol -0.005566960 0.96937817849958
## 26   sq_average_drink_per_day  0.116084716 0.65578352846414
## 27   sq_average_drink_per_day -0.261826993 0.53666952886876
## 28   sq_average_drink_per_day -0.105299660 0.40074255201137
## 29              sq_self_hep_b -0.141938612 0.21157942812386
## 30              sq_self_hep_b  0.192152070 0.44361557003896
## 31              sq_self_hep_c -0.146606869 0.19870154554602
## 32              sq_self_hep_c -0.155239261 0.52527191567200
## 33          supp_meds_tylenol  0.304940955 0.42547659640687
## 34          supp_meds_tylenol  0.419083324 0.50488516530130
## 35         supp_meds_steroids  0.040924100 0.90371151384786
## 36         supp_meds_steroids -0.657053844 0.53425913283768
## 37              sq_water_well -0.209121056 0.06225382264052
## 38              sq_water_well -0.218124485 0.15388307408752
## 39    sq_water_tap_unfiltered -0.023621223 0.86549139364515
## 40    sq_water_tap_unfiltered  0.242930596 0.06779584280842
## 41  sq_water_house_filtration -0.164768323 0.13133740209379
## 42  sq_water_house_filtration -0.015941373 0.93081104646968
## 43     sq_water_faucet_filter -0.137999833 0.25477840057300
## 44     sq_water_faucet_filter  0.058969782 0.65148799314024
## 45   sq_water_charcoal_filter -0.157835307 0.15697464465805
## 46   sq_water_charcoal_filter  0.078120988 0.61742210595572
## 47           sq_water_bottled -0.489245463 0.00049146516071
## 48           sq_water_bottled -0.425652852 0.00165296201584
## 49              sq_water_none -0.155707258 0.14497821238538
## 50              sq_water_none -0.401945941 0.10160522807343
## 51        sq_water_other_type -0.144238597 0.18408036183405
## 52        sq_water_other_type -0.171961692 0.40129154196832
## 53                     source -0.246320491 0.09903735645484
## 54                     source -0.089689998 0.48062363680237
## 55                     source -0.664123896 0.00983867718366
## 56                        sex -0.132717021 0.21831847824619
## 57             race_eth_label  0.124713526 0.62644193500231
## 58             race_eth_label  0.427845656 0.22209485749045
## 59             race_eth_label  0.219074045 0.37257706140279
## 60             race_eth_label -0.051398854 0.86705038235471
## 61           race_final_label -0.129041956 0.85548381268167
## 62           race_final_label -0.480227615 0.34013223492180
## 63           race_final_label  0.221625730 0.70227327313975
## 64           race_final_label  0.731311112 0.07689498379755
## 65           race_final_label -0.106015704 0.35862071309513
## 66           race_final_label  1.830449710 0.06776455127017
## 67           race_final_label -0.181875197 0.53812055995121
## 68           race_final_label -0.426284433 0.26576060685672
## 69                  ethnicity  0.199915847 0.40915967427517
## 70                  ethnicity -0.051398854 0.86694944980858
## 71                      rural -0.223213006 0.23079719614026
## 72                      rural -0.089919293 0.58161444509190
## 73                    smoking -0.477786491 0.00796269280740
## 74                    smoking -0.268518439 0.02067734372000
## 75           sq_drink_alcohol -0.207870279 0.17690069057810
## 76           sq_drink_alcohol -0.133574488 0.34875449247860
## 77           sq_drink_alcohol  0.343071427 0.01623256250437
## 78   sq_average_drink_per_day -0.065819563 0.79640617614151
## 79   sq_average_drink_per_day  0.309710825 0.45570924404055
## 80   sq_average_drink_per_day -0.449821686 0.00027963169345
## 81              sq_self_hep_b -0.195178728 0.08568444046719
## 82              sq_self_hep_b  0.124792657 0.61823627484439
## 83              sq_self_hep_c -0.188575571 0.09718837256862
## 84              sq_self_hep_c  0.293829902 0.22767940372033
## 85          supp_meds_tylenol -0.104426492 0.78494873278750
## 86          supp_meds_tylenol  0.137188835 0.82724883059650
## 87         supp_meds_steroids -0.209622339 0.53564564076427
## 88         supp_meds_steroids -0.222383053 0.83337724859734
## 89              sq_water_well -0.132687198 0.23787633145928
## 90              sq_water_well -0.097902158 0.52323933577829
## 91    sq_water_tap_unfiltered -0.071825166 0.60904913514096
## 92    sq_water_tap_unfiltered  0.047090239 0.72458169476826
## 93  sq_water_house_filtration -0.118485504 0.27832139702085
## 94  sq_water_house_filtration -0.069280874 0.70639209383939
## 95     sq_water_faucet_filter -0.111215023 0.35955630258943
## 96     sq_water_faucet_filter -0.003617243 0.97793718366327
## 97   sq_water_charcoal_filter -0.118284545 0.28976269305199
## 98   sq_water_charcoal_filter -0.030459708 0.84593988600084
## 99           sq_water_bottled -0.338884188 0.01623333367883
## 100          sq_water_bottled -0.326239577 0.01651137591491
## 101             sq_water_none -0.081531902 0.44683501274427
## 102             sq_water_none -0.161168516 0.51271635885510
## 103       sq_water_other_type -0.140356692 0.19632346742509
## 104       sq_water_other_type  0.005741845 0.97764091282822
## 105                    source -0.175369787 0.24029175304655
## 106                    source -0.001557516 0.99023454642434
## 107                    source -0.604201367 0.01887733932903
## 108                       sex -0.182491531 0.09022350808633
## 109            race_eth_label  0.111439098 0.66399374875135
## 110            race_eth_label  0.075383541 0.82971361421867
## 111            race_eth_label  0.235158486 0.33914499829761
## 112            race_eth_label  0.010202957 0.97352278690987
## 113          race_final_label -0.031005242 0.96535916817741
## 114          race_final_label -0.439367404 0.38636569597660
## 115          race_final_label -0.010423415 0.98576358357417
## 116          race_final_label -0.117074701 0.77822396547546
## 117          race_final_label -0.118282395 0.30947290327306
## 118          race_final_label  1.415335888 0.16063322156114
## 119          race_final_label -0.232301749 0.43522678347209
## 120          race_final_label -0.250188337 0.51661750631772
## 121                 ethnicity  0.190570814 0.43185377958703
## 122                 ethnicity  0.010202957 0.97349654239088
## 123                     rural -0.166572585 0.37123860525779
## 124                     rural -0.122277328 0.45397511461728
## 125                   smoking -0.550497166 0.00222769302932
## 126                   smoking -0.243033617 0.03579987987392
## 127          sq_drink_alcohol -0.152143531 0.32289373258877
## 128          sq_drink_alcohol -0.064578736 0.65051917779038
## 129          sq_drink_alcohol  0.395101987 0.00571563030052
## 130  sq_average_drink_per_day -0.179363348 0.48139369719177
## 131  sq_average_drink_per_day  0.339756072 0.41243345212406
## 132  sq_average_drink_per_day -0.475665430 0.00012072205421
## 133             sq_self_hep_b -0.169980812 0.13479409364304
## 134             sq_self_hep_b  0.077652727 0.75677422534222
## 135             sq_self_hep_c -0.153241706 0.17920855998162
## 136             sq_self_hep_c -0.041526636 0.86504317800144
## 137         supp_meds_tylenol  0.137831746 0.71761513785548
## 138         supp_meds_tylenol  1.045530092 0.09550981098713
## 139        supp_meds_steroids -0.308992929 0.36097388049451
## 140        supp_meds_steroids  0.101453855 0.92347692411989
## 141             sq_water_well -0.197910956 0.07794860503605
## 142             sq_water_well -0.145051446 0.34325528210842
## 143   sq_water_tap_unfiltered -0.066858483 0.63379349809383
## 144   sq_water_tap_unfiltered  0.077746821 0.56054814450977
## 145 sq_water_house_filtration -0.157196639 0.15011332667990
## 146 sq_water_house_filtration -0.063597366 0.72917960795944
## 147    sq_water_faucet_filter -0.167657482 0.16701503357658
## 148    sq_water_faucet_filter -0.062872816 0.63038370929638
## 149  sq_water_charcoal_filter -0.171482905 0.12453939373143
## 150  sq_water_charcoal_filter -0.088273734 0.57281724091754
## 151          sq_water_bottled -0.316950714 0.02481255320483
## 152          sq_water_bottled -0.257416023 0.05871655083207
## 153             sq_water_none -0.150395243 0.15986807660113
## 154             sq_water_none -0.280592971 0.25347435646347
## 155       sq_water_other_type -0.178793168 0.09958577804326
## 156       sq_water_other_type -0.063262997 0.75719660380251
## 157                    source -0.028123983 0.85071911056610
## 158                    source  0.065459503 0.60757504689612
## 159                    source -0.549343879 0.03293039529831
## 160                       sex  0.170180187 0.11421477651813
## 161            race_eth_label  0.177351427 0.48947961492581
## 162            race_eth_label  0.213688811 0.54222117666899
## 163            race_eth_label  0.220729261 0.36962368798292
## 164            race_eth_label -0.064254677 0.83445298522021
## 165          race_final_label -0.256501601 0.71719909410729
## 166          race_final_label -0.612124024 0.22393436309560
## 167          race_final_label  0.302218261 0.60200810793988
## 168          race_final_label  0.345060629 0.40285038365019
## 169          race_final_label -0.030336969 0.79256603065347
## 170          race_final_label  2.506331197 0.01250591825764
## 171          race_final_label -0.261501159 0.37590551468125
## 172          race_final_label -0.356932287 0.35102751784317
## 173                 ethnicity  0.207295824 0.39191644876826
## 174                 ethnicity -0.064254677 0.83404410390463
## 175                     rural -0.150503790 0.41945767850033
## 176                     rural -0.025677499 0.87510243365735
## 177                   smoking -0.568161472 0.00162483462452
## 178                   smoking -0.177642837 0.12484388703393
## 179          sq_drink_alcohol -0.092797903 0.55221901412800
## 180          sq_drink_alcohol -0.036172572 0.80250707464715
## 181          sq_drink_alcohol  0.250989822 0.08264764890084
## 182  sq_average_drink_per_day -0.147331304 0.56878498930872
## 183  sq_average_drink_per_day  0.137412767 0.74388410618384
## 184  sq_average_drink_per_day -0.309054336 0.01330787645467
## 185             sq_self_hep_b -0.096081724 0.39765419685309
## 186             sq_self_hep_b  0.273215641 0.27638203097615
## 187             sq_self_hep_c -0.088278490 0.43852931773111
## 188             sq_self_hep_c  0.266319451 0.27592232776750
## 189         supp_meds_tylenol  0.222254099 0.56129273508639
## 190         supp_meds_tylenol  0.490187456 0.43550301353661
## 191        supp_meds_steroids -0.001370466 0.99676971448587
## 192        supp_meds_steroids -0.083001213 0.93744563737665
## 193             sq_water_well -0.138214358 0.21869101547987
## 194             sq_water_well -0.146054748 0.34083898263585
## 195   sq_water_tap_unfiltered -0.108841289 0.43862900238366
## 196   sq_water_tap_unfiltered -0.031520281 0.81362118915628
## 197 sq_water_house_filtration -0.101441951 0.35332742478950
## 198 sq_water_house_filtration  0.028812444 0.87552600125445
## 199    sq_water_faucet_filter -0.144511349 0.23371706850435
## 200    sq_water_faucet_filter -0.115764438 0.37605746039475
## 201  sq_water_charcoal_filter -0.094357710 0.39811618261251
## 202  sq_water_charcoal_filter  0.078374853 0.61705027014983
## 203          sq_water_bottled -0.374389594 0.00776080774018
## 204          sq_water_bottled -0.398004138 0.00339692534565
## 205             sq_water_none -0.097152295 0.36444330562043
## 206             sq_water_none -0.221344056 0.36842362123646
## 207       sq_water_other_type -0.118651822 0.27482381292786
## 208       sq_water_other_type -0.128580014 0.53059508137586
## 209                    source  0.104342436 0.48142224323953
## 210                    source -0.253170703 0.04569477728083
## 211                    source  0.366718557 0.15030326977520
## 212                       sex  0.047539722 0.65952777755573
## 213            race_eth_label -0.292172691 0.24558725047750
## 214            race_eth_label  0.259040647 0.45098650537994
## 215            race_eth_label  0.197393303 0.41292192888075
## 216            race_eth_label -0.004429835 0.98826938151728
## 217          race_final_label  2.990353342 0.00001177078990
## 218          race_final_label -0.750763195 0.11711937574190
## 219          race_final_label -0.213625556 0.69833970735644
## 220          race_final_label -0.201060933 0.60825038534995
## 221          race_final_label -0.528106273 0.00000214067538
## 222          race_final_label  0.144466203 0.87920452393557
## 223          race_final_label -0.541876335 0.05422224390061
## 224          race_final_label -0.641720215 0.07841348875071
## 225                 ethnicity  0.052119485 0.82999524103982
## 226                 ethnicity -0.004429835 0.98850968884498
## 227                     rural  0.039818426 0.83089199323123
## 228                     rural  0.067323679 0.68047191960674
## 229                   smoking -0.258608572 0.15406242068696
## 230                   smoking -0.042662611 0.71521597801004
## 231          sq_drink_alcohol  0.135862805 0.38818440115655
## 232          sq_drink_alcohol  0.048099616 0.74153340009344
## 233          sq_drink_alcohol  0.065602936 0.65217578709575
## 234  sq_average_drink_per_day  0.343582611 0.18358567488179
## 235  sq_average_drink_per_day  1.133059764 0.00724023699402
## 236  sq_average_drink_per_day  0.112919552 0.36326518486072
## 237             sq_self_hep_b -0.005245175 0.96324302106208
## 238             sq_self_hep_b -0.157051829 0.53229219046350
## 239             sq_self_hep_c  0.076666646 0.50144498984546
## 240             sq_self_hep_c  0.256164632 0.29506967023342
## 241         supp_meds_tylenol  0.142366243 0.70995004114878
## 242         supp_meds_tylenol  0.095580883 0.87917492730683
## 243        supp_meds_steroids  0.106160935 0.75379520356563
## 244        supp_meds_steroids -0.006282312 0.99525978948955
## 245             sq_water_well -0.079017038 0.48242764822963
## 246             sq_water_well -0.029840353 0.84588876056705
## 247   sq_water_tap_unfiltered  0.039163075 0.78055819320376
## 248   sq_water_tap_unfiltered  0.059596919 0.65602092727267
## 249 sq_water_house_filtration -0.064832914 0.55318487403520
## 250 sq_water_house_filtration  0.044680352 0.80820617839498
## 251    sq_water_faucet_filter  0.045347249 0.70845488436321
## 252    sq_water_faucet_filter  0.153061672 0.24212063939652
## 253  sq_water_charcoal_filter -0.087033046 0.43594263519055
## 254  sq_water_charcoal_filter -0.132945406 0.39671140672906
## 255          sq_water_bottled -0.162599041 0.25016970657883
## 256          sq_water_bottled -0.218302665 0.10999177430940
## 257             sq_water_none -0.052129747 0.62687470346805
## 258             sq_water_none  0.041283762 0.86689577335487
## 259       sq_water_other_type -0.084331471 0.43780014503922
## 260       sq_water_other_type  0.059851386 0.77047252115802
## 261                    source -0.242092835 0.10550138874091
## 262                    source -0.314762573 0.01381394218896
## 263                    source -0.438709022 0.08785424567301
## 264                       sex -0.074198882 0.49160923227275
## 265            race_eth_label -0.014642162 0.95432754792565
## 266            race_eth_label -0.023386604 0.94662591167007
## 267            race_eth_label  0.216205940 0.37788131612548
## 268            race_eth_label  0.294469251 0.33681006421286
## 269          race_final_label -0.064977808 0.92729153707793
## 270          race_final_label -0.364982301 0.47055950006093
## 271          race_final_label -0.287203346 0.62210940950523
## 272          race_final_label -0.234821601 0.57120995522041
## 273          race_final_label -0.261517532 0.02465261727798
## 274          race_final_label -0.322124510 0.74855511160560
## 275          race_final_label -0.276092067 0.35256225658707
## 276          race_final_label -0.334369593 0.38492197181345
## 277                 ethnicity  0.135638436 0.57595190803645
## 278                 ethnicity  0.294469251 0.33814572280042
## 279                     rural  0.402235775 0.03046477151052
## 280                     rural -0.025462981 0.87544973896638
## 281                   smoking  0.028762081 0.87397920554473
## 282                   smoking -0.132220016 0.25890118865790
## 283          sq_drink_alcohol  0.030936089 0.84399176612941
## 284          sq_drink_alcohol -0.113543959 0.43588771007607
## 285          sq_drink_alcohol  0.042489490 0.77007949378540
## 286  sq_average_drink_per_day  0.001738499 0.99459668702332
## 287  sq_average_drink_per_day  1.434035309 0.00065732225553
## 288  sq_average_drink_per_day  0.001408930 0.99089640548375
## 289             sq_self_hep_b -0.161647304 0.15490547435844
## 290             sq_self_hep_b -0.231096311 0.35688641562230
## 291             sq_self_hep_c -0.110620492 0.33226578512771
## 292             sq_self_hep_c  0.117368598 0.63123268162753
## 293         supp_meds_tylenol  0.051114225 0.89376908860934
## 294         supp_meds_tylenol -0.100001513 0.87363945925546
## 295        supp_meds_steroids  0.067647998 0.84159227516948
## 296        supp_meds_steroids -0.135451785 0.89808056927625
## 297             sq_water_well  0.028668599 0.79878534040690
## 298             sq_water_well -0.083682739 0.58576338919295
## 299   sq_water_tap_unfiltered -0.048786994 0.72768354490861
## 300   sq_water_tap_unfiltered -0.196265781 0.14160342074545
## 301 sq_water_house_filtration  0.066166986 0.54472537125353
## 302 sq_water_house_filtration  0.183741850 0.31807308447128
## 303    sq_water_faucet_filter  0.084888011 0.48456664213754
## 304    sq_water_faucet_filter  0.059173337 0.65126163583327
## 305  sq_water_charcoal_filter  0.061960093 0.57934760076695
## 306  sq_water_charcoal_filter  0.086905004 0.57974234090502
## 307          sq_water_bottled  0.001506774 0.99150577538440
## 308          sq_water_bottled -0.113192372 0.40754821004133
## 309             sq_water_none  0.033612442 0.75393115719871
## 310             sq_water_none -0.083659173 0.73415922696711
## 311       sq_water_other_type  0.024303777 0.82315529229055
## 312       sq_water_other_type -0.047333276 0.81768277140598
## 313                    source -0.076284269 0.61053968426051
## 314                    source  0.069320160 0.58737751970846
## 315                    source -0.428567690 0.09643431102686
## 316                       sex -0.125164803 0.24571033496868
## 317            race_eth_label -0.149446479 0.55373366719946
## 318            race_eth_label  0.138177043 0.68861553399695
## 319            race_eth_label  0.298831816 0.21707705491636
## 320            race_eth_label  0.014561956 0.96159120427071
## 321          race_final_label  0.233611347 0.73943226114685
## 322          race_final_label -0.682571588 0.17165331000666
## 323          race_final_label  0.067011955 0.90716829723056
## 324          race_final_label  0.012995087 0.97464817278574
## 325          race_final_label -0.430966705 0.00019045927418
## 326          race_final_label  0.542360390 0.58429976766167
## 327          race_final_label -0.300302745 0.30534074418753
## 328          race_final_label  0.025747942 0.94588993724285
## 329                 ethnicity  0.156059572 0.51999177754676
## 330                 ethnicity  0.014561956 0.96220473628701
## 331                     rural -0.190148486 0.30702073717271
## 332                     rural -0.170630017 0.29578828788987
## 333                   smoking -0.409788834 0.02321464375482
## 334                   smoking -0.222632007 0.05574471466387
## 335          sq_drink_alcohol -0.111496089 0.46913030842951
## 336          sq_drink_alcohol -0.046732994 0.74326861358174
## 337          sq_drink_alcohol  0.409557468 0.00422640049175
## 338  sq_average_drink_per_day -0.040424653 0.87408657104043
## 339  sq_average_drink_per_day -0.260093248 0.53096833080426
## 340  sq_average_drink_per_day -0.481474629 0.00010257804470
## 341             sq_self_hep_b -0.178851753 0.11559742974388
## 342             sq_self_hep_b  0.045228192 0.85681327359605
## 343             sq_self_hep_c -0.177866607 0.11880220685087
## 344             sq_self_hep_c  0.005156484 0.98314670068817
## 345         supp_meds_tylenol  0.443801098 0.24563690847941
## 346         supp_meds_tylenol  0.770507972 0.21985255079337
## 347        supp_meds_steroids  0.107805082 0.75003621633081
## 348        supp_meds_steroids -0.380809440 0.71869901424882
## 349             sq_water_well -0.186536945 0.09675928899761
## 350             sq_water_well -0.112239398 0.46350632851723
## 351   sq_water_tap_unfiltered -0.007174993 0.95913876245625
## 352   sq_water_tap_unfiltered  0.175492994 0.18845245498274
## 353 sq_water_house_filtration -0.160648035 0.14111864812130
## 354 sq_water_house_filtration -0.184146789 0.31607063806375
## 355    sq_water_faucet_filter -0.180991093 0.13559656249832
## 356    sq_water_faucet_filter -0.144357201 0.26925087582704
## 357  sq_water_charcoal_filter -0.171264179 0.12499948414919
## 358  sq_water_charcoal_filter -0.044223355 0.77750970889268
## 359          sq_water_bottled -0.437282541 0.00182086074127
## 360          sq_water_bottled -0.459621966 0.00069540024129
## 361             sq_water_none -0.165431763 0.12150146864344
## 362             sq_water_none -0.392843913 0.10950059241962
## 363       sq_water_other_type -0.181037121 0.09537905447064
## 364       sq_water_other_type -0.037666105 0.85393082686463
## 365                    source -0.597901038 0.00005228868616
## 366                    source -0.265570259 0.03370576474755
## 367                    source  0.326611981 0.19403427471931
## 368                       sex -0.153686852 0.15386599131094
## 369            race_eth_label  0.072315457 0.77714097293777
## 370            race_eth_label  0.474258658 0.17483025510466
## 371            race_eth_label  0.086984175 0.72246058781372
## 372            race_eth_label  0.457678603 0.13554981673754
## 373          race_final_label  0.347296961 0.62303943070383
## 374          race_final_label  1.652976248 0.00107063992872
## 375          race_final_label -0.072385667 0.90034178273734
## 376          race_final_label -0.041298065 0.92002234501068
## 377          race_final_label  0.062986709 0.58422548881022
## 378          race_final_label -0.258911280 0.79509259208138
## 379          race_final_label -0.207098635 0.48201712804661
## 380          race_final_label -0.227823677 0.55058320024782
## 381                 ethnicity  0.099940992 0.67927159271312
## 382                 ethnicity  0.457678603 0.13566211156982
## 383                     rural -0.147231442 0.42822635251512
## 384                     rural -0.260727617 0.11000532282996
## 385                   smoking  0.216112013 0.23338823688727
## 386                   smoking -0.066187762 0.57137978178832
## 387          sq_drink_alcohol  0.101568743 0.51822776011725
## 388          sq_drink_alcohol -0.001902538 0.98957692131135
## 389          sq_drink_alcohol  0.151438614 0.29776677122813
## 390  sq_average_drink_per_day  0.057487391 0.82519940509489
## 391  sq_average_drink_per_day -0.344924347 0.41554963544340
## 392  sq_average_drink_per_day -0.132431733 0.29051139953655
## 393             sq_self_hep_b -0.127726808 0.26138986317014
## 394             sq_self_hep_b -0.189266149 0.45099558920554
## 395             sq_self_hep_c -0.124364032 0.27562127705554
## 396             sq_self_hep_c -0.191670281 0.43308498300754
## 397         supp_meds_tylenol  0.259503283 0.49765332661943
## 398         supp_meds_tylenol  0.077719005 0.90156876480205
## 399        supp_meds_steroids  0.217854838 0.51959775927010
## 400        supp_meds_steroids -0.290150953 0.78364104048900
## 401             sq_water_well -0.035396466 0.75308976935880
## 402             sq_water_well -0.004037423 0.97903005717147
## 403   sq_water_tap_unfiltered -0.227639599 0.10460564698569
## 404   sq_water_tap_unfiltered -0.209658682 0.11625097875513
## 405 sq_water_house_filtration  0.031800030 0.76900375265055
## 406 sq_water_house_filtration  0.499637112 0.00639538693209
## 407    sq_water_faucet_filter -0.007319188 0.95193128359767
## 408    sq_water_faucet_filter  0.073005971 0.57712410414268
## 409  sq_water_charcoal_filter -0.026450287 0.81297502605541
## 410  sq_water_charcoal_filter  0.038558107 0.80596269263471
## 411          sq_water_bottled -0.128076641 0.36598943670439
## 412          sq_water_bottled -0.100027498 0.46439951786495
## 413             sq_water_none -0.001346337 0.98995994083385
## 414             sq_water_none -0.333071106 0.17591982687334
## 415       sq_water_other_type  0.029917853 0.78311655900997
## 416       sq_water_other_type  0.149328289 0.46695697882131
## 417                    source -0.122335675 0.40741994614468
## 418                    source  0.166651022 0.18606871857654
## 419                    source -0.710857596 0.00529967763418
## 420                       sex -0.081072838 0.45234435016076
## 421            race_eth_label  0.388436412 0.12719466758624
## 422            race_eth_label  0.754963047 0.03031047612560
## 423            race_eth_label  0.309387448 0.20479634375197
## 424            race_eth_label -0.069642049 0.81923914578154
## 425          race_final_label -0.542170032 0.43999328372661
## 426          race_final_label -0.494735976 0.32117620165951
## 427          race_final_label  0.319841767 0.57762843834789
## 428          race_final_label  1.390329246 0.00073512345399
## 429          race_final_label  0.081404362 0.47660773266545
## 430          race_final_label  0.533002293 0.59053852024601
## 431          race_final_label -0.225942878 0.44010936421998
## 432          race_final_label -0.560240155 0.14005245576398
## 433                 ethnicity  0.353302356 0.14289994452075
## 434                 ethnicity -0.069642049 0.81947623415182
## 435                     rural -0.210790739 0.25793964453799
## 436                     rural -0.047194429 0.77247965322541
## 437                   smoking -0.559380006 0.00181874007454
## 438                   smoking -0.320177642 0.00560773476027
## 439          sq_drink_alcohol -0.177862327 0.24777442714208
## 440          sq_drink_alcohol -0.166639376 0.24258750496615
## 441          sq_drink_alcohol  0.340041634 0.01719459843916
## 442  sq_average_drink_per_day  0.167092869 0.51266621906040
## 443  sq_average_drink_per_day  0.035136353 0.93254748917460
## 444  sq_average_drink_per_day -0.427661820 0.00054676111079
## 445             sq_self_hep_b -0.227855130 0.04436660508740
## 446             sq_self_hep_b  0.243875526 0.32895313970868
## 447             sq_self_hep_c -0.231106095 0.04207291408647
## 448             sq_self_hep_c  0.220640012 0.36434293822168
## 449         supp_meds_tylenol -0.324432302 0.39565811530909
## 450         supp_meds_tylenol  0.265279446 0.67233383417726
## 451        supp_meds_steroids -0.436901492 0.19622409137501
## 452        supp_meds_steroids -0.021862618 0.98346705424688
## 453             sq_water_well -0.128171207 0.25427262322351
## 454             sq_water_well -0.024950983 0.87074988658916
## 455   sq_water_tap_unfiltered -0.222055106 0.11348990546405
## 456   sq_water_tap_unfiltered -0.070017536 0.59948647097833
## 457 sq_water_house_filtration -0.132638341 0.22481218952721
## 458 sq_water_house_filtration -0.039782055 0.82866854646979
## 459    sq_water_faucet_filter -0.131448800 0.27842603953197
## 460    sq_water_faucet_filter  0.029760120 0.81982321014451
## 461  sq_water_charcoal_filter -0.114798173 0.30392858519815
## 462  sq_water_charcoal_filter  0.052864139 0.73582611255727
## 463          sq_water_bottled -0.351141936 0.01283108050418
## 464          sq_water_bottled -0.281397807 0.03857335714817
## 465             sq_water_none -0.096455729 0.36794161779746
## 466             sq_water_none -0.209125261 0.39547290349843
## 467       sq_water_other_type -0.139208496 0.20007125981871
## 468       sq_water_other_type -0.078118811 0.70304722582391
## 469                    source  0.061680049 0.67844612713382
## 470                    source  0.228325732 0.07252736028874
## 471                    source -0.456481340 0.07474208092999
## 472                       sex  0.303500510 0.00469493718684
## 473            race_eth_label  0.171448227 0.50197497862571
## 474            race_eth_label -0.082866147 0.81223970154000
## 475            race_eth_label  0.359143079 0.14277455985686
## 476            race_eth_label  0.170940815 0.57643705503039
## 477          race_final_label -0.355957031 0.61712705883957
## 478          race_final_label -0.759880711 0.13339352193114
## 479          race_final_label -0.063258723 0.91352122741302
## 480          race_final_label -0.375470252 0.36528543014309
## 481          race_final_label -0.167859238 0.14835335109304
## 482          race_final_label  1.008017086 0.31601277293424
## 483          race_final_label -0.254488284 0.39135877967639
## 484          race_final_label -0.272328943 0.47898862867844
## 485                 ethnicity  0.282534341 0.24394937568137
## 486                 ethnicity  0.170940815 0.57772753504936
## 487                     rural -0.153308591 0.41080517756178
## 488                     rural -0.052872710 0.74619014229260
## 489                   smoking -0.597431528 0.00092276549008
## 490                   smoking -0.121792259 0.29205421769069
## 491          sq_drink_alcohol -0.065354458 0.67660542380994
## 492          sq_drink_alcohol  0.011750535 0.93549031572168
## 493          sq_drink_alcohol  0.208068661 0.15152091369329
## 494  sq_average_drink_per_day  0.015959027 0.95098333148844
## 495  sq_average_drink_per_day  0.063766417 0.88000354530749
## 496  sq_average_drink_per_day -0.216089727 0.08417452030593
## 497             sq_self_hep_b -0.022472789 0.84347580470059
## 498             sq_self_hep_b  0.146603021 0.55989141502025
## 499             sq_self_hep_c -0.028534432 0.80272136125276
## 500             sq_self_hep_c  0.026712887 0.91313950657492
## 501         supp_meds_tylenol  0.302693492 0.42836384684072
## 502         supp_meds_tylenol  0.781438896 0.21353816457676
## 503        supp_meds_steroids -0.105591434 0.75503614022517
## 504        supp_meds_steroids -0.466254373 0.65925938965953
## 505             sq_water_well -0.067772539 0.54651262733605
## 506             sq_water_well -0.155400137 0.31136152736213
## 507   sq_water_tap_unfiltered -0.007275703 0.95872659269935
## 508   sq_water_tap_unfiltered  0.026032274 0.84574417416999
## 509 sq_water_house_filtration -0.036057060 0.74164992928475
## 510 sq_water_house_filtration -0.016420265 0.92895252444545
## 511    sq_water_faucet_filter -0.078388895 0.51835547757239
## 512    sq_water_faucet_filter -0.124962156 0.33973975540600
## 513  sq_water_charcoal_filter -0.042810377 0.70176869616977
## 514  sq_water_charcoal_filter  0.024256103 0.87718241871529
## 515          sq_water_bottled -0.343434053 0.01441692733044
## 516          sq_water_bottled -0.433131801 0.00143238137611
## 517             sq_water_none -0.039586203 0.71147423698222
## 518             sq_water_none -0.320453635 0.19292650115117
## 519       sq_water_other_type -0.044492329 0.68223041086397
## 520       sq_water_other_type -0.166568984 0.41705478475211
## 521                    source  0.003415976 0.98179146397575
## 522                    source  0.045020692 0.72434393181778
## 523                    source -0.512003703 0.04708117779278
## 524                       sex -0.023593908 0.82693773382376
## 525            race_eth_label  0.060863687 0.81187103175203
## 526            race_eth_label  0.283568579 0.41725220517750
## 527            race_eth_label  0.170801740 0.48604179615838
## 528            race_eth_label -0.239941118 0.43384263785675
## 529          race_final_label -0.462599423 0.51535911876491
## 530          race_final_label -0.486996060 0.33501616397590
## 531          race_final_label  0.006627957 0.99090900750855
## 532          race_final_label  0.762330953 0.06613992103282
## 533          race_final_label -0.107573068 0.35324429414613
## 534          race_final_label -0.151427581 0.88002788778056
## 535          race_final_label -0.076316098 0.79678214569844
## 536          race_final_label -0.540407962 0.15992207734304
## 537                 ethnicity  0.142621379 0.55500838863838
## 538                 ethnicity -0.239941118 0.43331808561261
## 539                     rural -0.132863570 0.47604978557856
## 540                     rural  0.004063870 0.98015694799679
## 541                   smoking -0.339253335 0.06089981157603
## 542                   smoking -0.160431500 0.16912075431888
## 543          sq_drink_alcohol  0.033696841 0.82844187593956
## 544          sq_drink_alcohol  0.039289037 0.78511449567258
## 545          sq_drink_alcohol  0.377990514 0.00887878150753
## 546  sq_average_drink_per_day -0.146498661 0.56947537328378
## 547  sq_average_drink_per_day -0.006154150 0.98827941703331
## 548  sq_average_drink_per_day -0.377444112 0.00245565678436
## 549             sq_self_hep_b -0.141051588 0.21493465638587
## 550             sq_self_hep_b  0.042106924 0.86677177612830
## 551             sq_self_hep_c -0.099793531 0.37976264387495
## 552             sq_self_hep_c  0.428406768 0.07911871955042
## 553         supp_meds_tylenol -0.241743315 0.52552310304422
## 554         supp_meds_tylenol  0.743002153 0.23522327501678
## 555        supp_meds_steroids -0.218523659 0.51797858027497
## 556        supp_meds_steroids  0.714542748 0.49868186690490
## 557             sq_water_well  0.011686060 0.91721458430984
## 558             sq_water_well  0.126086020 0.41156166219780
## 559   sq_water_tap_unfiltered -0.092200494 0.51184853848188
## 560   sq_water_tap_unfiltered -0.075435399 0.57279182355582
## 561 sq_water_house_filtration -0.011928675 0.91310090818640
## 562 sq_water_house_filtration -0.129770756 0.48093371950682
## 563    sq_water_faucet_filter -0.027285058 0.82219076468471
## 564    sq_water_faucet_filter -0.090228579 0.49076539977289
## 565  sq_water_charcoal_filter  0.036827190 0.74186846476578
## 566  sq_water_charcoal_filter  0.040403136 0.79688719547919
## 567          sq_water_bottled -0.177736099 0.20792533592252
## 568          sq_water_bottled -0.275383208 0.04357849187013
## 569             sq_water_none  0.022297708 0.83524455521135
## 570             sq_water_none -0.129187361 0.59997849432490
## 571       sq_water_other_type -0.060323288 0.57889504928244
## 572       sq_water_other_type  0.092365555 0.65262891764172
## 573                    source  0.128461465 0.38687468700135
## 574                    source  0.397209096 0.00181891699049
## 575                    source  0.041555547 0.87053458291734
## 576                       sex  0.081196520 0.45165441374254
## 577            race_eth_label -0.005861819 0.98166500714231
## 578            race_eth_label  0.072383603 0.83548591495917
## 579            race_eth_label  0.279024063 0.25418843077719
## 580            race_eth_label  0.047160447 0.87739425723636
## 581          race_final_label  1.360287326 0.05400990074290
## 582          race_final_label -0.677922478 0.17574960546630
## 583          race_final_label -0.050667294 0.92993187406726
## 584          race_final_label -0.433484494 0.29078243295854
## 585          race_final_label -0.304808265 0.00818424840147
## 586          race_final_label  0.211835891 0.83120895797992
## 587          race_final_label -0.366394274 0.21245914434555
## 588          race_final_label -0.371598373 0.32895717410029
## 589                 ethnicity  0.183595676 0.44910052978292
## 590                 ethnicity  0.047160447 0.87801175679233
## 591                     rural -0.293698353 0.11397432465806
## 592                     rural -0.225951890 0.16528201759410
## 593                   smoking -0.328121339 0.07025007004598
## 594                   smoking -0.086475186 0.45886310938747
## 595          sq_drink_alcohol  0.008721390 0.95557605731021
## 596          sq_drink_alcohol  0.053297528 0.71336276014425
## 597          sq_drink_alcohol  0.264186688 0.06864932704253
## 598  sq_average_drink_per_day  0.345986259 0.18167914444427
## 599  sq_average_drink_per_day -0.077065092 0.85474769829995
## 600  sq_average_drink_per_day -0.187162580 0.13320907616193
## 601             sq_self_hep_b -0.043841269 0.70010452722554
## 602             sq_self_hep_b -0.148523564 0.55475236942286
## 603             sq_self_hep_c -0.008504837 0.94062361844989
## 604             sq_self_hep_c -0.133253026 0.58632012495494
## 605         supp_meds_tylenol  0.181507556 0.63535705748588
## 606         supp_meds_tylenol  0.167413591 0.79002340503006
## 607        supp_meds_steroids  0.162567452 0.63085743718607
## 608        supp_meds_steroids -0.402999024 0.70298916766313
## 609             sq_water_well -0.031775990 0.77734871293318
## 610             sq_water_well -0.161526945 0.29269446086610
## 611   sq_water_tap_unfiltered  0.130887263 0.34954163790135
## 612   sq_water_tap_unfiltered  0.265440810 0.04667168825859
## 613 sq_water_house_filtration  0.035397161 0.74609488302110
## 614 sq_water_house_filtration  0.122866958 0.50458013295510
## 615    sq_water_faucet_filter  0.074224982 0.54112145438895
## 616    sq_water_faucet_filter  0.046001885 0.72532178061484
## 617  sq_water_charcoal_filter -0.017015905 0.87904866822130
## 618  sq_water_charcoal_filter -0.007147878 0.96368572588700
## 619          sq_water_bottled -0.303379520 0.02943808106098
## 620          sq_water_bottled -0.527135357 0.00009910794049
## 621             sq_water_none  0.032209065 0.76347440250853
## 622             sq_water_none -0.286379175 0.24456352714546
## 623       sq_water_other_type -0.000698204 0.99487761581831
## 624       sq_water_other_type  0.047675534 0.81640595739470
## 625                    source  0.518825224 0.00035296753017
## 626                    source  0.273293280 0.02652537691163
## 627                    source  1.365624166 0.00000006218287
## 628                       sex -0.152564255 0.15690319949755
## 629            race_eth_label -0.099619137 0.69832721067773
## 630            race_eth_label -0.280687329 0.42434022335422
## 631            race_eth_label -0.155025713 0.52926880834616
## 632            race_eth_label -0.240179832 0.43570815119400
## 633          race_final_label  0.363145761 0.60936886944373
## 634          race_final_label -0.125495861 0.80359047025395
## 635          race_final_label  0.085954218 0.88247237807533
## 636          race_final_label -0.557711314 0.17818156233011
## 637          race_final_label  0.057141508 0.62161421926663
## 638          race_final_label  2.154326215 0.03221506802430
## 639          race_final_label -0.132608948 0.65440808798253
## 640          race_final_label  0.322560344 0.40092858744280
## 641                 ethnicity -0.143914516 0.55309406174543
## 642                 ethnicity -0.240179832 0.43478718661802
## 643                     rural -0.030728063 0.86877802201808
## 644                     rural  0.227249815 0.16396371383201
## 645                   smoking  0.383823460 0.03156290235867
## 646                   smoking  0.424432242 0.00024963802757
## 647          sq_drink_alcohol -0.106510076 0.49296709044419
## 648          sq_drink_alcohol  0.296946616 0.03963369285390
## 649          sq_drink_alcohol -0.085498716 0.55172036530078
## 650  sq_average_drink_per_day -0.104427621 0.68793945814010
## 651  sq_average_drink_per_day -0.273190945 0.51845922867124
## 652  sq_average_drink_per_day  0.139826105 0.26388573205593
## 653             sq_self_hep_b  0.338679517 0.00270288931959
## 654             sq_self_hep_b -0.238220118 0.33688740607012
## 655             sq_self_hep_c  0.386303960 0.00064482875449
## 656             sq_self_hep_c  0.339735501 0.15897129077816
## 657         supp_meds_tylenol -0.039791052 0.91717244608162
## 658         supp_meds_tylenol -0.354387040 0.57295049357703
## 659        supp_meds_steroids  0.173481308 0.60813457682163
## 660        supp_meds_steroids  0.657077748 0.53424295732900
## 661             sq_water_well  0.284585889 0.01091883956148
## 662             sq_water_well -0.056920712 0.70803138909272
## 663   sq_water_tap_unfiltered  0.372682966 0.00767897644492
## 664   sq_water_tap_unfiltered  0.132440345 0.31755980881214
## 665 sq_water_house_filtration  0.276526321 0.01100469577270
## 666 sq_water_house_filtration -0.062346803 0.73243980970183
## 667    sq_water_faucet_filter  0.331222398 0.00611511609349
## 668    sq_water_faucet_filter  0.096415171 0.45709065759293
## 669  sq_water_charcoal_filter  0.289475271 0.00910866204876
## 670  sq_water_charcoal_filter -0.085752388 0.58051667684540
## 671          sq_water_bottled  0.329643039 0.01932347445562
## 672          sq_water_bottled  0.069930043 0.60590779765293
## 673             sq_water_none  0.279475921 0.00867219569147
## 674             sq_water_none -0.229269416 0.34669199788674
## 675       sq_water_other_type  0.337214768 0.00169010311525
## 676       sq_water_other_type  0.565255198 0.00524748730282
## 677                    source -0.506598992 0.00049247622953
## 678                    source -0.713318581 0.00000001380182
## 679                    source -0.676877491 0.00655977315887
## 680                       sex  0.319078694 0.00293897166878
## 681            race_eth_label -0.384561281 0.13013569457742
## 682            race_eth_label -0.015459945 0.96442794455499
## 683            race_eth_label -0.177342612 0.46598140195592
## 684            race_eth_label  0.282335829 0.35335867587746
## 685          race_final_label  1.135611694 0.10806768285307
## 686          race_final_label -0.425309066 0.39610942244147
## 687          race_final_label -0.471448517 0.41428300467105
## 688          race_final_label  0.306275468 0.45604501729654
## 689          race_final_label -0.307432538 0.00776078864512
## 690          race_final_label -0.471448517 0.63586430273092
## 691          race_final_label -0.420749965 0.15305394925531
## 692          race_final_label -0.167562266 0.66018676901763
## 693                 ethnicity -0.232734365 0.33347886020211
## 694                 ethnicity  0.282335829 0.35448966539951
## 695                     rural -0.017570768 0.92489389560955
## 696                     rural -0.109517642 0.50284536393231
## 697                   smoking -0.179856476 0.32177091020604
## 698                   smoking -0.072533826 0.53558707695853
## 699          sq_drink_alcohol  0.327969771 0.03594188193303
## 700          sq_drink_alcohol  0.024728206 0.86408706078347
## 701          sq_drink_alcohol -0.067189872 0.64121543008338
## 702  sq_average_drink_per_day -0.270412395 0.29799235545930
## 703  sq_average_drink_per_day  0.222094164 0.59912099850931
## 704  sq_average_drink_per_day  0.139006224 0.26608530903584
## 705             sq_self_hep_b -0.080549601 0.47899561405699
## 706             sq_self_hep_b  0.123863430 0.62208712770531
## 707             sq_self_hep_c -0.033324941 0.77046630225326
## 708             sq_self_hep_c -0.013533496 0.95592885498328
## 709         supp_meds_tylenol  0.074785778 0.84501740098413
## 710         supp_meds_tylenol -0.270640942 0.66676216096643
## 711        supp_meds_steroids  0.146786904 0.66450267863285
## 712        supp_meds_steroids -0.055286439 0.95829689341887
## 713             sq_water_well -0.071131278 0.52721094260777
## 714             sq_water_well -0.020271407 0.89496451658351
## 715   sq_water_tap_unfiltered -0.081146477 0.56352426626744
## 716   sq_water_tap_unfiltered -0.119942521 0.36983338632043
## 717 sq_water_house_filtration -0.029969350 0.78307622374691
## 718 sq_water_house_filtration  0.322655831 0.07896650237649
## 719    sq_water_faucet_filter  0.098297135 0.41138610856269
## 720    sq_water_faucet_filter  0.429929406 0.00093424336604
## 721  sq_water_charcoal_filter -0.120332622 0.27988058283521
## 722  sq_water_charcoal_filter -0.289534580 0.06445662344530
## 723          sq_water_bottled -0.103467380 0.46520637575138
## 724          sq_water_bottled -0.107528668 0.43170112368954
## 725             sq_water_none -0.018050259 0.86604968749351
## 726             sq_water_none  0.311629483 0.20541041566927
## 727       sq_water_other_type -0.069122669 0.52440830804744
## 728       sq_water_other_type -0.229725344 0.26277924482141
##                                                   Factor         PFAS
## 1                                             sourceDUKE pf_hx_s_scld
## 2                                             sourceNCSU pf_hx_s_scld
## 3                                              sourceUNC pf_hx_s_scld
## 4                                                sexMale pf_hx_s_scld
## 5                                      race_eth_labelNHB pf_hx_s_scld
## 6                                      race_eth_labelNHO pf_hx_s_scld
## 7                                      race_eth_labelNHW pf_hx_s_scld
## 8                     race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 9                        race_final_labelAmerican Indian pf_hx_s_scld
## 10        race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 11                                 race_final_labelAsian pf_hx_s_scld
## 12                race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 13                                 race_final_labelBlack pf_hx_s_scld
## 14                    race_final_labelMore than one race pf_hx_s_scld
## 15                                 race_final_labelOther pf_hx_s_scld
## 16                  race_final_labelUnknown/Not Reported pf_hx_s_scld
## 17                                 ethnicityNot Hispanic pf_hx_s_scld
## 18                         ethnicityUnknown/Not Reported pf_hx_s_scld
## 19                             ruralLiving in rural area pf_hx_s_scld
## 20                             ruralUnknown/Not Reported pf_hx_s_scld
## 21                              smokingSmoke or use vape pf_hx_s_scld
## 22                           smokingUnknown/Not Reported pf_hx_s_scld
## 23          sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 24                  sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 25                  sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 26  sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 27  sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 28          sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 29                     sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 30                                      sq_self_hep_bYes pf_hx_s_scld
## 31                     sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 32                                      sq_self_hep_cYes pf_hx_s_scld
## 33                 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 34                                  supp_meds_tylenolYes pf_hx_s_scld
## 35                supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 36                                 supp_meds_steroidsYes pf_hx_s_scld
## 37                     sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 38                                      sq_water_wellYes pf_hx_s_scld
## 39           sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 40                            sq_water_tap_unfilteredYes pf_hx_s_scld
## 41         sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 42                          sq_water_house_filtrationYes pf_hx_s_scld
## 43            sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 44                             sq_water_faucet_filterYes pf_hx_s_scld
## 45          sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 46                           sq_water_charcoal_filterYes pf_hx_s_scld
## 47                  sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 48                                   sq_water_bottledYes pf_hx_s_scld
## 49                     sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 50                                      sq_water_noneYes pf_hx_s_scld
## 51               sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 52                                sq_water_other_typeYes pf_hx_s_scld
## 53                                            sourceDUKE    pfda_scld
## 54                                            sourceNCSU    pfda_scld
## 55                                             sourceUNC    pfda_scld
## 56                                               sexMale    pfda_scld
## 57                                     race_eth_labelNHB    pfda_scld
## 58                                     race_eth_labelNHO    pfda_scld
## 59                                     race_eth_labelNHW    pfda_scld
## 60                    race_eth_labelUnknown/Not Reported    pfda_scld
## 61                       race_final_labelAmerican Indian    pfda_scld
## 62        race_final_labelAmerican Indian/Alaskan Native    pfda_scld
## 63                                 race_final_labelAsian    pfda_scld
## 64                race_final_labelAsian/Pacific Islander    pfda_scld
## 65                                 race_final_labelBlack    pfda_scld
## 66                    race_final_labelMore than one race    pfda_scld
## 67                                 race_final_labelOther    pfda_scld
## 68                  race_final_labelUnknown/Not Reported    pfda_scld
## 69                                 ethnicityNot Hispanic    pfda_scld
## 70                         ethnicityUnknown/Not Reported    pfda_scld
## 71                             ruralLiving in rural area    pfda_scld
## 72                             ruralUnknown/Not Reported    pfda_scld
## 73                              smokingSmoke or use vape    pfda_scld
## 74                           smokingUnknown/Not Reported    pfda_scld
## 75          sq_drink_alcoholNo, former drinker (stopped)    pfda_scld
## 76                  sq_drink_alcoholUnknown/Not Reported    pfda_scld
## 77                  sq_drink_alcoholYes, current drinker    pfda_scld
## 78  sq_average_drink_per_day1-2 alcoholic drinks per day    pfda_scld
## 79  sq_average_drink_per_day3-4 alcoholic drinks per day    pfda_scld
## 80          sq_average_drink_per_dayUnknown/Not Reported    pfda_scld
## 81                     sq_self_hep_bUnknown/Not Reported    pfda_scld
## 82                                      sq_self_hep_bYes    pfda_scld
## 83                     sq_self_hep_cUnknown/Not Reported    pfda_scld
## 84                                      sq_self_hep_cYes    pfda_scld
## 85                 supp_meds_tylenolUnknown/Not Reported    pfda_scld
## 86                                  supp_meds_tylenolYes    pfda_scld
## 87                supp_meds_steroidsUnknown/Not Reported    pfda_scld
## 88                                 supp_meds_steroidsYes    pfda_scld
## 89                     sq_water_wellUnknown/Not Reported    pfda_scld
## 90                                      sq_water_wellYes    pfda_scld
## 91           sq_water_tap_unfilteredUnknown/Not Reported    pfda_scld
## 92                            sq_water_tap_unfilteredYes    pfda_scld
## 93         sq_water_house_filtrationUnknown/Not Reported    pfda_scld
## 94                          sq_water_house_filtrationYes    pfda_scld
## 95            sq_water_faucet_filterUnknown/Not Reported    pfda_scld
## 96                             sq_water_faucet_filterYes    pfda_scld
## 97          sq_water_charcoal_filterUnknown/Not Reported    pfda_scld
## 98                           sq_water_charcoal_filterYes    pfda_scld
## 99                  sq_water_bottledUnknown/Not Reported    pfda_scld
## 100                                  sq_water_bottledYes    pfda_scld
## 101                    sq_water_noneUnknown/Not Reported    pfda_scld
## 102                                     sq_water_noneYes    pfda_scld
## 103              sq_water_other_typeUnknown/Not Reported    pfda_scld
## 104                               sq_water_other_typeYes    pfda_scld
## 105                                           sourceDUKE    pfna_scld
## 106                                           sourceNCSU    pfna_scld
## 107                                            sourceUNC    pfna_scld
## 108                                              sexMale    pfna_scld
## 109                                    race_eth_labelNHB    pfna_scld
## 110                                    race_eth_labelNHO    pfna_scld
## 111                                    race_eth_labelNHW    pfna_scld
## 112                   race_eth_labelUnknown/Not Reported    pfna_scld
## 113                      race_final_labelAmerican Indian    pfna_scld
## 114       race_final_labelAmerican Indian/Alaskan Native    pfna_scld
## 115                                race_final_labelAsian    pfna_scld
## 116               race_final_labelAsian/Pacific Islander    pfna_scld
## 117                                race_final_labelBlack    pfna_scld
## 118                   race_final_labelMore than one race    pfna_scld
## 119                                race_final_labelOther    pfna_scld
## 120                 race_final_labelUnknown/Not Reported    pfna_scld
## 121                                ethnicityNot Hispanic    pfna_scld
## 122                        ethnicityUnknown/Not Reported    pfna_scld
## 123                            ruralLiving in rural area    pfna_scld
## 124                            ruralUnknown/Not Reported    pfna_scld
## 125                             smokingSmoke or use vape    pfna_scld
## 126                          smokingUnknown/Not Reported    pfna_scld
## 127         sq_drink_alcoholNo, former drinker (stopped)    pfna_scld
## 128                 sq_drink_alcoholUnknown/Not Reported    pfna_scld
## 129                 sq_drink_alcoholYes, current drinker    pfna_scld
## 130 sq_average_drink_per_day1-2 alcoholic drinks per day    pfna_scld
## 131 sq_average_drink_per_day3-4 alcoholic drinks per day    pfna_scld
## 132         sq_average_drink_per_dayUnknown/Not Reported    pfna_scld
## 133                    sq_self_hep_bUnknown/Not Reported    pfna_scld
## 134                                     sq_self_hep_bYes    pfna_scld
## 135                    sq_self_hep_cUnknown/Not Reported    pfna_scld
## 136                                     sq_self_hep_cYes    pfna_scld
## 137                supp_meds_tylenolUnknown/Not Reported    pfna_scld
## 138                                 supp_meds_tylenolYes    pfna_scld
## 139               supp_meds_steroidsUnknown/Not Reported    pfna_scld
## 140                                supp_meds_steroidsYes    pfna_scld
## 141                    sq_water_wellUnknown/Not Reported    pfna_scld
## 142                                     sq_water_wellYes    pfna_scld
## 143          sq_water_tap_unfilteredUnknown/Not Reported    pfna_scld
## 144                           sq_water_tap_unfilteredYes    pfna_scld
## 145        sq_water_house_filtrationUnknown/Not Reported    pfna_scld
## 146                         sq_water_house_filtrationYes    pfna_scld
## 147           sq_water_faucet_filterUnknown/Not Reported    pfna_scld
## 148                            sq_water_faucet_filterYes    pfna_scld
## 149         sq_water_charcoal_filterUnknown/Not Reported    pfna_scld
## 150                          sq_water_charcoal_filterYes    pfna_scld
## 151                 sq_water_bottledUnknown/Not Reported    pfna_scld
## 152                                  sq_water_bottledYes    pfna_scld
## 153                    sq_water_noneUnknown/Not Reported    pfna_scld
## 154                                     sq_water_noneYes    pfna_scld
## 155              sq_water_other_typeUnknown/Not Reported    pfna_scld
## 156                               sq_water_other_typeYes    pfna_scld
## 157                                           sourceDUKE    pfos_scld
## 158                                           sourceNCSU    pfos_scld
## 159                                            sourceUNC    pfos_scld
## 160                                              sexMale    pfos_scld
## 161                                    race_eth_labelNHB    pfos_scld
## 162                                    race_eth_labelNHO    pfos_scld
## 163                                    race_eth_labelNHW    pfos_scld
## 164                   race_eth_labelUnknown/Not Reported    pfos_scld
## 165                      race_final_labelAmerican Indian    pfos_scld
## 166       race_final_labelAmerican Indian/Alaskan Native    pfos_scld
## 167                                race_final_labelAsian    pfos_scld
## 168               race_final_labelAsian/Pacific Islander    pfos_scld
## 169                                race_final_labelBlack    pfos_scld
## 170                   race_final_labelMore than one race    pfos_scld
## 171                                race_final_labelOther    pfos_scld
## 172                 race_final_labelUnknown/Not Reported    pfos_scld
## 173                                ethnicityNot Hispanic    pfos_scld
## 174                        ethnicityUnknown/Not Reported    pfos_scld
## 175                            ruralLiving in rural area    pfos_scld
## 176                            ruralUnknown/Not Reported    pfos_scld
## 177                             smokingSmoke or use vape    pfos_scld
## 178                          smokingUnknown/Not Reported    pfos_scld
## 179         sq_drink_alcoholNo, former drinker (stopped)    pfos_scld
## 180                 sq_drink_alcoholUnknown/Not Reported    pfos_scld
## 181                 sq_drink_alcoholYes, current drinker    pfos_scld
## 182 sq_average_drink_per_day1-2 alcoholic drinks per day    pfos_scld
## 183 sq_average_drink_per_day3-4 alcoholic drinks per day    pfos_scld
## 184         sq_average_drink_per_dayUnknown/Not Reported    pfos_scld
## 185                    sq_self_hep_bUnknown/Not Reported    pfos_scld
## 186                                     sq_self_hep_bYes    pfos_scld
## 187                    sq_self_hep_cUnknown/Not Reported    pfos_scld
## 188                                     sq_self_hep_cYes    pfos_scld
## 189                supp_meds_tylenolUnknown/Not Reported    pfos_scld
## 190                                 supp_meds_tylenolYes    pfos_scld
## 191               supp_meds_steroidsUnknown/Not Reported    pfos_scld
## 192                                supp_meds_steroidsYes    pfos_scld
## 193                    sq_water_wellUnknown/Not Reported    pfos_scld
## 194                                     sq_water_wellYes    pfos_scld
## 195          sq_water_tap_unfilteredUnknown/Not Reported    pfos_scld
## 196                           sq_water_tap_unfilteredYes    pfos_scld
## 197        sq_water_house_filtrationUnknown/Not Reported    pfos_scld
## 198                         sq_water_house_filtrationYes    pfos_scld
## 199           sq_water_faucet_filterUnknown/Not Reported    pfos_scld
## 200                            sq_water_faucet_filterYes    pfos_scld
## 201         sq_water_charcoal_filterUnknown/Not Reported    pfos_scld
## 202                          sq_water_charcoal_filterYes    pfos_scld
## 203                 sq_water_bottledUnknown/Not Reported    pfos_scld
## 204                                  sq_water_bottledYes    pfos_scld
## 205                    sq_water_noneUnknown/Not Reported    pfos_scld
## 206                                     sq_water_noneYes    pfos_scld
## 207              sq_water_other_typeUnknown/Not Reported    pfos_scld
## 208                               sq_water_other_typeYes    pfos_scld
## 209                                           sourceDUKE pf_hp_a_scld
## 210                                           sourceNCSU pf_hp_a_scld
## 211                                            sourceUNC pf_hp_a_scld
## 212                                              sexMale pf_hp_a_scld
## 213                                    race_eth_labelNHB pf_hp_a_scld
## 214                                    race_eth_labelNHO pf_hp_a_scld
## 215                                    race_eth_labelNHW pf_hp_a_scld
## 216                   race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 217                      race_final_labelAmerican Indian pf_hp_a_scld
## 218       race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 219                                race_final_labelAsian pf_hp_a_scld
## 220               race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 221                                race_final_labelBlack pf_hp_a_scld
## 222                   race_final_labelMore than one race pf_hp_a_scld
## 223                                race_final_labelOther pf_hp_a_scld
## 224                 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 225                                ethnicityNot Hispanic pf_hp_a_scld
## 226                        ethnicityUnknown/Not Reported pf_hp_a_scld
## 227                            ruralLiving in rural area pf_hp_a_scld
## 228                            ruralUnknown/Not Reported pf_hp_a_scld
## 229                             smokingSmoke or use vape pf_hp_a_scld
## 230                          smokingUnknown/Not Reported pf_hp_a_scld
## 231         sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 232                 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 233                 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 234 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 235 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 236         sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 237                    sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 238                                     sq_self_hep_bYes pf_hp_a_scld
## 239                    sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 240                                     sq_self_hep_cYes pf_hp_a_scld
## 241                supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 242                                 supp_meds_tylenolYes pf_hp_a_scld
## 243               supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 244                                supp_meds_steroidsYes pf_hp_a_scld
## 245                    sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 246                                     sq_water_wellYes pf_hp_a_scld
## 247          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 248                           sq_water_tap_unfilteredYes pf_hp_a_scld
## 249        sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 250                         sq_water_house_filtrationYes pf_hp_a_scld
## 251           sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 252                            sq_water_faucet_filterYes pf_hp_a_scld
## 253         sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 254                          sq_water_charcoal_filterYes pf_hp_a_scld
## 255                 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 256                                  sq_water_bottledYes pf_hp_a_scld
## 257                    sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 258                                     sq_water_noneYes pf_hp_a_scld
## 259              sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 260                               sq_water_other_typeYes pf_hp_a_scld
## 261                                           sourceDUKE    pfbs_scld
## 262                                           sourceNCSU    pfbs_scld
## 263                                            sourceUNC    pfbs_scld
## 264                                              sexMale    pfbs_scld
## 265                                    race_eth_labelNHB    pfbs_scld
## 266                                    race_eth_labelNHO    pfbs_scld
## 267                                    race_eth_labelNHW    pfbs_scld
## 268                   race_eth_labelUnknown/Not Reported    pfbs_scld
## 269                      race_final_labelAmerican Indian    pfbs_scld
## 270       race_final_labelAmerican Indian/Alaskan Native    pfbs_scld
## 271                                race_final_labelAsian    pfbs_scld
## 272               race_final_labelAsian/Pacific Islander    pfbs_scld
## 273                                race_final_labelBlack    pfbs_scld
## 274                   race_final_labelMore than one race    pfbs_scld
## 275                                race_final_labelOther    pfbs_scld
## 276                 race_final_labelUnknown/Not Reported    pfbs_scld
## 277                                ethnicityNot Hispanic    pfbs_scld
## 278                        ethnicityUnknown/Not Reported    pfbs_scld
## 279                            ruralLiving in rural area    pfbs_scld
## 280                            ruralUnknown/Not Reported    pfbs_scld
## 281                             smokingSmoke or use vape    pfbs_scld
## 282                          smokingUnknown/Not Reported    pfbs_scld
## 283         sq_drink_alcoholNo, former drinker (stopped)    pfbs_scld
## 284                 sq_drink_alcoholUnknown/Not Reported    pfbs_scld
## 285                 sq_drink_alcoholYes, current drinker    pfbs_scld
## 286 sq_average_drink_per_day1-2 alcoholic drinks per day    pfbs_scld
## 287 sq_average_drink_per_day3-4 alcoholic drinks per day    pfbs_scld
## 288         sq_average_drink_per_dayUnknown/Not Reported    pfbs_scld
## 289                    sq_self_hep_bUnknown/Not Reported    pfbs_scld
## 290                                     sq_self_hep_bYes    pfbs_scld
## 291                    sq_self_hep_cUnknown/Not Reported    pfbs_scld
## 292                                     sq_self_hep_cYes    pfbs_scld
## 293                supp_meds_tylenolUnknown/Not Reported    pfbs_scld
## 294                                 supp_meds_tylenolYes    pfbs_scld
## 295               supp_meds_steroidsUnknown/Not Reported    pfbs_scld
## 296                                supp_meds_steroidsYes    pfbs_scld
## 297                    sq_water_wellUnknown/Not Reported    pfbs_scld
## 298                                     sq_water_wellYes    pfbs_scld
## 299          sq_water_tap_unfilteredUnknown/Not Reported    pfbs_scld
## 300                           sq_water_tap_unfilteredYes    pfbs_scld
## 301        sq_water_house_filtrationUnknown/Not Reported    pfbs_scld
## 302                         sq_water_house_filtrationYes    pfbs_scld
## 303           sq_water_faucet_filterUnknown/Not Reported    pfbs_scld
## 304                            sq_water_faucet_filterYes    pfbs_scld
## 305         sq_water_charcoal_filterUnknown/Not Reported    pfbs_scld
## 306                          sq_water_charcoal_filterYes    pfbs_scld
## 307                 sq_water_bottledUnknown/Not Reported    pfbs_scld
## 308                                  sq_water_bottledYes    pfbs_scld
## 309                    sq_water_noneUnknown/Not Reported    pfbs_scld
## 310                                     sq_water_noneYes    pfbs_scld
## 311              sq_water_other_typeUnknown/Not Reported    pfbs_scld
## 312                               sq_water_other_typeYes    pfbs_scld
## 313                                           sourceDUKE    pfoa_scld
## 314                                           sourceNCSU    pfoa_scld
## 315                                            sourceUNC    pfoa_scld
## 316                                              sexMale    pfoa_scld
## 317                                    race_eth_labelNHB    pfoa_scld
## 318                                    race_eth_labelNHO    pfoa_scld
## 319                                    race_eth_labelNHW    pfoa_scld
## 320                   race_eth_labelUnknown/Not Reported    pfoa_scld
## 321                      race_final_labelAmerican Indian    pfoa_scld
## 322       race_final_labelAmerican Indian/Alaskan Native    pfoa_scld
## 323                                race_final_labelAsian    pfoa_scld
## 324               race_final_labelAsian/Pacific Islander    pfoa_scld
## 325                                race_final_labelBlack    pfoa_scld
## 326                   race_final_labelMore than one race    pfoa_scld
## 327                                race_final_labelOther    pfoa_scld
## 328                 race_final_labelUnknown/Not Reported    pfoa_scld
## 329                                ethnicityNot Hispanic    pfoa_scld
## 330                        ethnicityUnknown/Not Reported    pfoa_scld
## 331                            ruralLiving in rural area    pfoa_scld
## 332                            ruralUnknown/Not Reported    pfoa_scld
## 333                             smokingSmoke or use vape    pfoa_scld
## 334                          smokingUnknown/Not Reported    pfoa_scld
## 335         sq_drink_alcoholNo, former drinker (stopped)    pfoa_scld
## 336                 sq_drink_alcoholUnknown/Not Reported    pfoa_scld
## 337                 sq_drink_alcoholYes, current drinker    pfoa_scld
## 338 sq_average_drink_per_day1-2 alcoholic drinks per day    pfoa_scld
## 339 sq_average_drink_per_day3-4 alcoholic drinks per day    pfoa_scld
## 340         sq_average_drink_per_dayUnknown/Not Reported    pfoa_scld
## 341                    sq_self_hep_bUnknown/Not Reported    pfoa_scld
## 342                                     sq_self_hep_bYes    pfoa_scld
## 343                    sq_self_hep_cUnknown/Not Reported    pfoa_scld
## 344                                     sq_self_hep_cYes    pfoa_scld
## 345                supp_meds_tylenolUnknown/Not Reported    pfoa_scld
## 346                                 supp_meds_tylenolYes    pfoa_scld
## 347               supp_meds_steroidsUnknown/Not Reported    pfoa_scld
## 348                                supp_meds_steroidsYes    pfoa_scld
## 349                    sq_water_wellUnknown/Not Reported    pfoa_scld
## 350                                     sq_water_wellYes    pfoa_scld
## 351          sq_water_tap_unfilteredUnknown/Not Reported    pfoa_scld
## 352                           sq_water_tap_unfilteredYes    pfoa_scld
## 353        sq_water_house_filtrationUnknown/Not Reported    pfoa_scld
## 354                         sq_water_house_filtrationYes    pfoa_scld
## 355           sq_water_faucet_filterUnknown/Not Reported    pfoa_scld
## 356                            sq_water_faucet_filterYes    pfoa_scld
## 357         sq_water_charcoal_filterUnknown/Not Reported    pfoa_scld
## 358                          sq_water_charcoal_filterYes    pfoa_scld
## 359                 sq_water_bottledUnknown/Not Reported    pfoa_scld
## 360                                  sq_water_bottledYes    pfoa_scld
## 361                    sq_water_noneUnknown/Not Reported    pfoa_scld
## 362                                     sq_water_noneYes    pfoa_scld
## 363              sq_water_other_typeUnknown/Not Reported    pfoa_scld
## 364                               sq_water_other_typeYes    pfoa_scld
## 365                                           sourceDUKE pf_pe_a_scld
## 366                                           sourceNCSU pf_pe_a_scld
## 367                                            sourceUNC pf_pe_a_scld
## 368                                              sexMale pf_pe_a_scld
## 369                                    race_eth_labelNHB pf_pe_a_scld
## 370                                    race_eth_labelNHO pf_pe_a_scld
## 371                                    race_eth_labelNHW pf_pe_a_scld
## 372                   race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 373                      race_final_labelAmerican Indian pf_pe_a_scld
## 374       race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 375                                race_final_labelAsian pf_pe_a_scld
## 376               race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 377                                race_final_labelBlack pf_pe_a_scld
## 378                   race_final_labelMore than one race pf_pe_a_scld
## 379                                race_final_labelOther pf_pe_a_scld
## 380                 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 381                                ethnicityNot Hispanic pf_pe_a_scld
## 382                        ethnicityUnknown/Not Reported pf_pe_a_scld
## 383                            ruralLiving in rural area pf_pe_a_scld
## 384                            ruralUnknown/Not Reported pf_pe_a_scld
## 385                             smokingSmoke or use vape pf_pe_a_scld
## 386                          smokingUnknown/Not Reported pf_pe_a_scld
## 387         sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 388                 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 389                 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 390 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 391 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 392         sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 393                    sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 394                                     sq_self_hep_bYes pf_pe_a_scld
## 395                    sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 396                                     sq_self_hep_cYes pf_pe_a_scld
## 397                supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 398                                 supp_meds_tylenolYes pf_pe_a_scld
## 399               supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 400                                supp_meds_steroidsYes pf_pe_a_scld
## 401                    sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 402                                     sq_water_wellYes pf_pe_a_scld
## 403          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 404                           sq_water_tap_unfilteredYes pf_pe_a_scld
## 405        sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 406                         sq_water_house_filtrationYes pf_pe_a_scld
## 407           sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 408                            sq_water_faucet_filterYes pf_pe_a_scld
## 409         sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 410                          sq_water_charcoal_filterYes pf_pe_a_scld
## 411                 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 412                                  sq_water_bottledYes pf_pe_a_scld
## 413                    sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 414                                     sq_water_noneYes pf_pe_a_scld
## 415              sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 416                               sq_water_other_typeYes pf_pe_a_scld
## 417                                           sourceDUKE pf_un_a_scld
## 418                                           sourceNCSU pf_un_a_scld
## 419                                            sourceUNC pf_un_a_scld
## 420                                              sexMale pf_un_a_scld
## 421                                    race_eth_labelNHB pf_un_a_scld
## 422                                    race_eth_labelNHO pf_un_a_scld
## 423                                    race_eth_labelNHW pf_un_a_scld
## 424                   race_eth_labelUnknown/Not Reported pf_un_a_scld
## 425                      race_final_labelAmerican Indian pf_un_a_scld
## 426       race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 427                                race_final_labelAsian pf_un_a_scld
## 428               race_final_labelAsian/Pacific Islander pf_un_a_scld
## 429                                race_final_labelBlack pf_un_a_scld
## 430                   race_final_labelMore than one race pf_un_a_scld
## 431                                race_final_labelOther pf_un_a_scld
## 432                 race_final_labelUnknown/Not Reported pf_un_a_scld
## 433                                ethnicityNot Hispanic pf_un_a_scld
## 434                        ethnicityUnknown/Not Reported pf_un_a_scld
## 435                            ruralLiving in rural area pf_un_a_scld
## 436                            ruralUnknown/Not Reported pf_un_a_scld
## 437                             smokingSmoke or use vape pf_un_a_scld
## 438                          smokingUnknown/Not Reported pf_un_a_scld
## 439         sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 440                 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 441                 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 442 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 443 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 444         sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 445                    sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 446                                     sq_self_hep_bYes pf_un_a_scld
## 447                    sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 448                                     sq_self_hep_cYes pf_un_a_scld
## 449                supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 450                                 supp_meds_tylenolYes pf_un_a_scld
## 451               supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 452                                supp_meds_steroidsYes pf_un_a_scld
## 453                    sq_water_wellUnknown/Not Reported pf_un_a_scld
## 454                                     sq_water_wellYes pf_un_a_scld
## 455          sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 456                           sq_water_tap_unfilteredYes pf_un_a_scld
## 457        sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 458                         sq_water_house_filtrationYes pf_un_a_scld
## 459           sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 460                            sq_water_faucet_filterYes pf_un_a_scld
## 461         sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 462                          sq_water_charcoal_filterYes pf_un_a_scld
## 463                 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 464                                  sq_water_bottledYes pf_un_a_scld
## 465                    sq_water_noneUnknown/Not Reported pf_un_a_scld
## 466                                     sq_water_noneYes pf_un_a_scld
## 467              sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 468                               sq_water_other_typeYes pf_un_a_scld
## 469                                           sourceDUKE pf_hp_s_scld
## 470                                           sourceNCSU pf_hp_s_scld
## 471                                            sourceUNC pf_hp_s_scld
## 472                                              sexMale pf_hp_s_scld
## 473                                    race_eth_labelNHB pf_hp_s_scld
## 474                                    race_eth_labelNHO pf_hp_s_scld
## 475                                    race_eth_labelNHW pf_hp_s_scld
## 476                   race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 477                      race_final_labelAmerican Indian pf_hp_s_scld
## 478       race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 479                                race_final_labelAsian pf_hp_s_scld
## 480               race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 481                                race_final_labelBlack pf_hp_s_scld
## 482                   race_final_labelMore than one race pf_hp_s_scld
## 483                                race_final_labelOther pf_hp_s_scld
## 484                 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 485                                ethnicityNot Hispanic pf_hp_s_scld
## 486                        ethnicityUnknown/Not Reported pf_hp_s_scld
## 487                            ruralLiving in rural area pf_hp_s_scld
## 488                            ruralUnknown/Not Reported pf_hp_s_scld
## 489                             smokingSmoke or use vape pf_hp_s_scld
## 490                          smokingUnknown/Not Reported pf_hp_s_scld
## 491         sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 492                 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 493                 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 494 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 495 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 496         sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 497                    sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 498                                     sq_self_hep_bYes pf_hp_s_scld
## 499                    sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 500                                     sq_self_hep_cYes pf_hp_s_scld
## 501                supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 502                                 supp_meds_tylenolYes pf_hp_s_scld
## 503               supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 504                                supp_meds_steroidsYes pf_hp_s_scld
## 505                    sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 506                                     sq_water_wellYes pf_hp_s_scld
## 507          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 508                           sq_water_tap_unfilteredYes pf_hp_s_scld
## 509        sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 510                         sq_water_house_filtrationYes pf_hp_s_scld
## 511           sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 512                            sq_water_faucet_filterYes pf_hp_s_scld
## 513         sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 514                          sq_water_charcoal_filterYes pf_hp_s_scld
## 515                 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 516                                  sq_water_bottledYes pf_hp_s_scld
## 517                    sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 518                                     sq_water_noneYes pf_hp_s_scld
## 519              sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 520                               sq_water_other_typeYes pf_hp_s_scld
## 521                                           sourceDUKE pf_do_a_scld
## 522                                           sourceNCSU pf_do_a_scld
## 523                                            sourceUNC pf_do_a_scld
## 524                                              sexMale pf_do_a_scld
## 525                                    race_eth_labelNHB pf_do_a_scld
## 526                                    race_eth_labelNHO pf_do_a_scld
## 527                                    race_eth_labelNHW pf_do_a_scld
## 528                   race_eth_labelUnknown/Not Reported pf_do_a_scld
## 529                      race_final_labelAmerican Indian pf_do_a_scld
## 530       race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 531                                race_final_labelAsian pf_do_a_scld
## 532               race_final_labelAsian/Pacific Islander pf_do_a_scld
## 533                                race_final_labelBlack pf_do_a_scld
## 534                   race_final_labelMore than one race pf_do_a_scld
## 535                                race_final_labelOther pf_do_a_scld
## 536                 race_final_labelUnknown/Not Reported pf_do_a_scld
## 537                                ethnicityNot Hispanic pf_do_a_scld
## 538                        ethnicityUnknown/Not Reported pf_do_a_scld
## 539                            ruralLiving in rural area pf_do_a_scld
## 540                            ruralUnknown/Not Reported pf_do_a_scld
## 541                             smokingSmoke or use vape pf_do_a_scld
## 542                          smokingUnknown/Not Reported pf_do_a_scld
## 543         sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 544                 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 545                 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 546 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 547 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 548         sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 549                    sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 550                                     sq_self_hep_bYes pf_do_a_scld
## 551                    sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 552                                     sq_self_hep_cYes pf_do_a_scld
## 553                supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 554                                 supp_meds_tylenolYes pf_do_a_scld
## 555               supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 556                                supp_meds_steroidsYes pf_do_a_scld
## 557                    sq_water_wellUnknown/Not Reported pf_do_a_scld
## 558                                     sq_water_wellYes pf_do_a_scld
## 559          sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 560                           sq_water_tap_unfilteredYes pf_do_a_scld
## 561        sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 562                         sq_water_house_filtrationYes pf_do_a_scld
## 563           sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 564                            sq_water_faucet_filterYes pf_do_a_scld
## 565         sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 566                          sq_water_charcoal_filterYes pf_do_a_scld
## 567                 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 568                                  sq_water_bottledYes pf_do_a_scld
## 569                    sq_water_noneUnknown/Not Reported pf_do_a_scld
## 570                                     sq_water_noneYes pf_do_a_scld
## 571              sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 572                               sq_water_other_typeYes pf_do_a_scld
## 573                                           sourceDUKE pf_pe_s_scld
## 574                                           sourceNCSU pf_pe_s_scld
## 575                                            sourceUNC pf_pe_s_scld
## 576                                              sexMale pf_pe_s_scld
## 577                                    race_eth_labelNHB pf_pe_s_scld
## 578                                    race_eth_labelNHO pf_pe_s_scld
## 579                                    race_eth_labelNHW pf_pe_s_scld
## 580                   race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 581                      race_final_labelAmerican Indian pf_pe_s_scld
## 582       race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 583                                race_final_labelAsian pf_pe_s_scld
## 584               race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 585                                race_final_labelBlack pf_pe_s_scld
## 586                   race_final_labelMore than one race pf_pe_s_scld
## 587                                race_final_labelOther pf_pe_s_scld
## 588                 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 589                                ethnicityNot Hispanic pf_pe_s_scld
## 590                        ethnicityUnknown/Not Reported pf_pe_s_scld
## 591                            ruralLiving in rural area pf_pe_s_scld
## 592                            ruralUnknown/Not Reported pf_pe_s_scld
## 593                             smokingSmoke or use vape pf_pe_s_scld
## 594                          smokingUnknown/Not Reported pf_pe_s_scld
## 595         sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 596                 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 597                 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 598 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 599 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 600         sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 601                    sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 602                                     sq_self_hep_bYes pf_pe_s_scld
## 603                    sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 604                                     sq_self_hep_cYes pf_pe_s_scld
## 605                supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 606                                 supp_meds_tylenolYes pf_pe_s_scld
## 607               supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 608                                supp_meds_steroidsYes pf_pe_s_scld
## 609                    sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 610                                     sq_water_wellYes pf_pe_s_scld
## 611          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 612                           sq_water_tap_unfilteredYes pf_pe_s_scld
## 613        sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 614                         sq_water_house_filtrationYes pf_pe_s_scld
## 615           sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 616                            sq_water_faucet_filterYes pf_pe_s_scld
## 617         sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 618                          sq_water_charcoal_filterYes pf_pe_s_scld
## 619                 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 620                                  sq_water_bottledYes pf_pe_s_scld
## 621                    sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 622                                     sq_water_noneYes pf_pe_s_scld
## 623              sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 624                               sq_water_other_typeYes pf_pe_s_scld
## 625                                           sourceDUKE pf_hx_a_scld
## 626                                           sourceNCSU pf_hx_a_scld
## 627                                            sourceUNC pf_hx_a_scld
## 628                                              sexMale pf_hx_a_scld
## 629                                    race_eth_labelNHB pf_hx_a_scld
## 630                                    race_eth_labelNHO pf_hx_a_scld
## 631                                    race_eth_labelNHW pf_hx_a_scld
## 632                   race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 633                      race_final_labelAmerican Indian pf_hx_a_scld
## 634       race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 635                                race_final_labelAsian pf_hx_a_scld
## 636               race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 637                                race_final_labelBlack pf_hx_a_scld
## 638                   race_final_labelMore than one race pf_hx_a_scld
## 639                                race_final_labelOther pf_hx_a_scld
## 640                 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 641                                ethnicityNot Hispanic pf_hx_a_scld
## 642                        ethnicityUnknown/Not Reported pf_hx_a_scld
## 643                            ruralLiving in rural area pf_hx_a_scld
## 644                            ruralUnknown/Not Reported pf_hx_a_scld
## 645                             smokingSmoke or use vape pf_hx_a_scld
## 646                          smokingUnknown/Not Reported pf_hx_a_scld
## 647         sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 648                 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 649                 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 650 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 651 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 652         sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 653                    sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 654                                     sq_self_hep_bYes pf_hx_a_scld
## 655                    sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 656                                     sq_self_hep_cYes pf_hx_a_scld
## 657                supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 658                                 supp_meds_tylenolYes pf_hx_a_scld
## 659               supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 660                                supp_meds_steroidsYes pf_hx_a_scld
## 661                    sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 662                                     sq_water_wellYes pf_hx_a_scld
## 663          sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 664                           sq_water_tap_unfilteredYes pf_hx_a_scld
## 665        sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 666                         sq_water_house_filtrationYes pf_hx_a_scld
## 667           sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 668                            sq_water_faucet_filterYes pf_hx_a_scld
## 669         sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 670                          sq_water_charcoal_filterYes pf_hx_a_scld
## 671                 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 672                                  sq_water_bottledYes pf_hx_a_scld
## 673                    sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 674                                     sq_water_noneYes pf_hx_a_scld
## 675              sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 676                               sq_water_other_typeYes pf_hx_a_scld
## 677                                           sourceDUKE    pfba_scld
## 678                                           sourceNCSU    pfba_scld
## 679                                            sourceUNC    pfba_scld
## 680                                              sexMale    pfba_scld
## 681                                    race_eth_labelNHB    pfba_scld
## 682                                    race_eth_labelNHO    pfba_scld
## 683                                    race_eth_labelNHW    pfba_scld
## 684                   race_eth_labelUnknown/Not Reported    pfba_scld
## 685                      race_final_labelAmerican Indian    pfba_scld
## 686       race_final_labelAmerican Indian/Alaskan Native    pfba_scld
## 687                                race_final_labelAsian    pfba_scld
## 688               race_final_labelAsian/Pacific Islander    pfba_scld
## 689                                race_final_labelBlack    pfba_scld
## 690                   race_final_labelMore than one race    pfba_scld
## 691                                race_final_labelOther    pfba_scld
## 692                 race_final_labelUnknown/Not Reported    pfba_scld
## 693                                ethnicityNot Hispanic    pfba_scld
## 694                        ethnicityUnknown/Not Reported    pfba_scld
## 695                            ruralLiving in rural area    pfba_scld
## 696                            ruralUnknown/Not Reported    pfba_scld
## 697                             smokingSmoke or use vape    pfba_scld
## 698                          smokingUnknown/Not Reported    pfba_scld
## 699         sq_drink_alcoholNo, former drinker (stopped)    pfba_scld
## 700                 sq_drink_alcoholUnknown/Not Reported    pfba_scld
## 701                 sq_drink_alcoholYes, current drinker    pfba_scld
## 702 sq_average_drink_per_day1-2 alcoholic drinks per day    pfba_scld
## 703 sq_average_drink_per_day3-4 alcoholic drinks per day    pfba_scld
## 704         sq_average_drink_per_dayUnknown/Not Reported    pfba_scld
## 705                    sq_self_hep_bUnknown/Not Reported    pfba_scld
## 706                                     sq_self_hep_bYes    pfba_scld
## 707                    sq_self_hep_cUnknown/Not Reported    pfba_scld
## 708                                     sq_self_hep_cYes    pfba_scld
## 709                supp_meds_tylenolUnknown/Not Reported    pfba_scld
## 710                                 supp_meds_tylenolYes    pfba_scld
## 711               supp_meds_steroidsUnknown/Not Reported    pfba_scld
## 712                                supp_meds_steroidsYes    pfba_scld
## 713                    sq_water_wellUnknown/Not Reported    pfba_scld
## 714                                     sq_water_wellYes    pfba_scld
## 715          sq_water_tap_unfilteredUnknown/Not Reported    pfba_scld
## 716                           sq_water_tap_unfilteredYes    pfba_scld
## 717        sq_water_house_filtrationUnknown/Not Reported    pfba_scld
## 718                         sq_water_house_filtrationYes    pfba_scld
## 719           sq_water_faucet_filterUnknown/Not Reported    pfba_scld
## 720                            sq_water_faucet_filterYes    pfba_scld
## 721         sq_water_charcoal_filterUnknown/Not Reported    pfba_scld
## 722                          sq_water_charcoal_filterYes    pfba_scld
## 723                 sq_water_bottledUnknown/Not Reported    pfba_scld
## 724                                  sq_water_bottledYes    pfba_scld
## 725                    sq_water_noneUnknown/Not Reported    pfba_scld
## 726                                     sq_water_noneYes    pfba_scld
## 727              sq_water_other_typeUnknown/Not Reported    pfba_scld
## 728                               sq_water_other_typeYes    pfba_scld
# Combine and print all results
all_results <- rbind(continuous_results, categorical_results)
print(all_results)
##                   Confounders          Coeff                   P
## 1           age_at_enrollment  0.02303963456 0.00002886969906048
## 2                         bmi -0.01294812141 0.09611802745084295
## 3                 trig_mg_d_l  0.00006026119 0.92894071463283046
## 4           age_at_enrollment  0.01401689114 0.01151280974826796
## 5                         bmi -0.01953451965 0.01049324625441954
## 6                 trig_mg_d_l -0.00087134772 0.19560135743360868
## 7           age_at_enrollment  0.02285242310 0.00003418692654294
## 8                         bmi -0.01016268100 0.19162630103330469
## 9                 trig_mg_d_l -0.00012960498 0.84543151201780353
## 10          age_at_enrollment  0.03015972790 0.00000003849922364
## 11                        bmi -0.00632410823 0.39797483121906918
## 12                trig_mg_d_l -0.00060423845 0.36564464944825859
## 13          age_at_enrollment  0.00610856935 0.27012846280853531
## 14                        bmi -0.00353210464 0.63621285405847949
## 15                trig_mg_d_l -0.00037474559 0.57810255513241993
## 16          age_at_enrollment -0.00039411970 0.94418956366096030
## 17                        bmi  0.00294487490 0.67943161804413910
## 18                trig_mg_d_l -0.00051069442 0.45289086346786955
## 19          age_at_enrollment  0.01990379953 0.00032706545799936
## 20                        bmi -0.01850220830 0.01730193122393180
## 21                trig_mg_d_l  0.00025406806 0.70465471480120978
## 22          age_at_enrollment  0.00422728729 0.41715088028540981
## 23                        bmi -0.00405085209 0.60753830998090086
## 24                trig_mg_d_l -0.00053365608 0.39127771559889879
## 25          age_at_enrollment  0.01474075004 0.00829630670610244
## 26                        bmi -0.02788390573 0.00016089041529066
## 27                trig_mg_d_l -0.00105386273 0.11863558257777872
## 28          age_at_enrollment  0.03647003290 0.00000000001254972
## 29                        bmi -0.00498536122 0.49783998070122160
## 30                trig_mg_d_l  0.00035106604 0.59487932766741591
## 31          age_at_enrollment  0.00666942172 0.23478386028671305
## 32                        bmi -0.02691555479 0.00053014713321087
## 33                trig_mg_d_l -0.00088518107 0.19110820055049224
## 34          age_at_enrollment  0.00723125430 0.18396989965273475
## 35                        bmi -0.02551872568 0.00081610677927414
## 36                trig_mg_d_l -0.00011850725 0.86073672780481414
## 37          age_at_enrollment -0.00767217344 0.16771220778328327
## 38                        bmi  0.00258183929 0.73804930867047069
## 39                trig_mg_d_l -0.00082944038 0.21370329383697809
## 40          age_at_enrollment  0.00156141004 0.75969057121585326
## 41                        bmi -0.00261010632 0.73748118274195495
## 42                trig_mg_d_l -0.00015137036 0.80919075133267548
## 43                     source  0.20089579824 0.17816637785405615
## 44                     source  0.28773100400 0.02406241741592427
## 45                     source -0.24039891626 0.34812012829126060
## 46                        sex  0.18719769132 0.08219582251875361
## 47             race_eth_label  0.05078849200 0.84233629638900953
## 48             race_eth_label -0.11510427163 0.74149107535181924
## 49             race_eth_label  0.27422981845 0.26292213105598811
## 50             race_eth_label  0.03888580877 0.89888254812528012
## 51           race_final_label -0.12541197098 0.86037830965615614
## 52           race_final_label -0.56765432777 0.26261635127196636
## 53           race_final_label -0.09411026424 0.87184652297268461
## 54           race_final_label -0.40797332376 0.32604479871633008
## 55           race_final_label -0.20988620230 0.07137795642001048
## 56           race_final_label -0.26436616917 0.79275654669035089
## 57           race_final_label -0.29339427221 0.32387441802348305
## 58           race_final_label -0.14073047032 0.71483775593414878
## 59                  ethnicity  0.18917716778 0.43536968718843172
## 60                  ethnicity  0.03888580877 0.89927233218110325
## 61                      rural -0.24655106670 0.18530426138296391
## 62                      rural -0.14787709652 0.36453370981273614
## 63                    smoking -0.32381027411 0.07305486987872799
## 64                    smoking -0.22948580632 0.04908826819920185
## 65           sq_drink_alcohol -0.09194220808 0.55776878080576808
## 66           sq_drink_alcohol -0.22484955476 0.12238274703819219
## 67           sq_drink_alcohol -0.00556695961 0.96937817849957508
## 68   sq_average_drink_per_day  0.11608471601 0.65578352846413657
## 69   sq_average_drink_per_day -0.26182699346 0.53666952886876229
## 70   sq_average_drink_per_day -0.10529965991 0.40074255201137199
## 71              sq_self_hep_b -0.14193861153 0.21157942812386105
## 72              sq_self_hep_b  0.19215207005 0.44361557003895957
## 73              sq_self_hep_c -0.14660686900 0.19870154554602260
## 74              sq_self_hep_c -0.15523926134 0.52527191567200049
## 75          supp_meds_tylenol  0.30494095510 0.42547659640687141
## 76          supp_meds_tylenol  0.41908332375 0.50488516530129868
## 77         supp_meds_steroids  0.04092410029 0.90371151384786230
## 78         supp_meds_steroids -0.65705384429 0.53425913283768089
## 79              sq_water_well -0.20912105580 0.06225382264052396
## 80              sq_water_well -0.21812448525 0.15388307408751842
## 81    sq_water_tap_unfiltered -0.02362122330 0.86549139364514827
## 82    sq_water_tap_unfiltered  0.24293059625 0.06779584280841820
## 83  sq_water_house_filtration -0.16476832282 0.13133740209378553
## 84  sq_water_house_filtration -0.01594137263 0.93081104646967838
## 85     sq_water_faucet_filter -0.13799983326 0.25477840057299572
## 86     sq_water_faucet_filter  0.05896978212 0.65148799314023809
## 87   sq_water_charcoal_filter -0.15783530672 0.15697464465805316
## 88   sq_water_charcoal_filter  0.07812098769 0.61742210595572367
## 89           sq_water_bottled -0.48924546335 0.00049146516070939
## 90           sq_water_bottled -0.42565285199 0.00165296201584316
## 91              sq_water_none -0.15570725838 0.14497821238538158
## 92              sq_water_none -0.40194594145 0.10160522807343024
## 93        sq_water_other_type -0.14423859690 0.18408036183404880
## 94        sq_water_other_type -0.17196169235 0.40129154196832417
## 95                     source -0.24632049084 0.09903735645483616
## 96                     source -0.08968999822 0.48062363680237086
## 97                     source -0.66412389592 0.00983867718366343
## 98                        sex -0.13271702075 0.21831847824619438
## 99             race_eth_label  0.12471352645 0.62644193500230960
## 100            race_eth_label  0.42784565581 0.22209485749044836
## 101            race_eth_label  0.21907404512 0.37257706140278501
## 102            race_eth_label -0.05139885416 0.86705038235471044
## 103          race_final_label -0.12904195608 0.85548381268167228
## 104          race_final_label -0.48022761545 0.34013223492179612
## 105          race_final_label  0.22162573034 0.70227327313975429
## 106          race_final_label  0.73131111208 0.07689498379754887
## 107          race_final_label -0.10601570408 0.35862071309513155
## 108          race_final_label  1.83044970969 0.06776455127016796
## 109          race_final_label -0.18187519687 0.53812055995120500
## 110          race_final_label -0.42628443262 0.26576060685671926
## 111                 ethnicity  0.19991584684 0.40915967427517430
## 112                 ethnicity -0.05139885416 0.86694944980857691
## 113                     rural -0.22321300609 0.23079719614026042
## 114                     rural -0.08991929292 0.58161444509189819
## 115                   smoking -0.47778649069 0.00796269280740328
## 116                   smoking -0.26851843856 0.02067734371999552
## 117          sq_drink_alcohol -0.20787027884 0.17690069057809690
## 118          sq_drink_alcohol -0.13357448819 0.34875449247860013
## 119          sq_drink_alcohol  0.34307142692 0.01623256250436837
## 120  sq_average_drink_per_day -0.06581956258 0.79640617614150799
## 121  sq_average_drink_per_day  0.30971082539 0.45570924404054869
## 122  sq_average_drink_per_day -0.44982168589 0.00027963169345085
## 123             sq_self_hep_b -0.19517872802 0.08568444046719309
## 124             sq_self_hep_b  0.12479265729 0.61823627484439159
## 125             sq_self_hep_c -0.18857557102 0.09718837256862224
## 126             sq_self_hep_c  0.29382990208 0.22767940372032977
## 127         supp_meds_tylenol -0.10442649184 0.78494873278749755
## 128         supp_meds_tylenol  0.13718883533 0.82724883059649901
## 129        supp_meds_steroids -0.20962233873 0.53564564076426890
## 130        supp_meds_steroids -0.22238305274 0.83337724859734486
## 131             sq_water_well -0.13268719802 0.23787633145927936
## 132             sq_water_well -0.09790215802 0.52323933577829118
## 133   sq_water_tap_unfiltered -0.07182516640 0.60904913514095971
## 134   sq_water_tap_unfiltered  0.04709023866 0.72458169476826395
## 135 sq_water_house_filtration -0.11848550442 0.27832139702085046
## 136 sq_water_house_filtration -0.06928087412 0.70639209383938706
## 137    sq_water_faucet_filter -0.11121502339 0.35955630258942806
## 138    sq_water_faucet_filter -0.00361724267 0.97793718366327420
## 139  sq_water_charcoal_filter -0.11828454491 0.28976269305198821
## 140  sq_water_charcoal_filter -0.03045970838 0.84593988600083936
## 141          sq_water_bottled -0.33888418838 0.01623333367882546
## 142          sq_water_bottled -0.32623957681 0.01651137591491339
## 143             sq_water_none -0.08153190201 0.44683501274426518
## 144             sq_water_none -0.16116851633 0.51271635885510181
## 145       sq_water_other_type -0.14035669239 0.19632346742509224
## 146       sq_water_other_type  0.00574184469 0.97764091282821886
## 147                    source -0.17536978665 0.24029175304654887
## 148                    source -0.00155751589 0.99023454642433784
## 149                    source -0.60420136676 0.01887733932902677
## 150                       sex -0.18249153129 0.09022350808632779
## 151            race_eth_label  0.11143909821 0.66399374875135109
## 152            race_eth_label  0.07538354085 0.82971361421867285
## 153            race_eth_label  0.23515848590 0.33914499829760592
## 154            race_eth_label  0.01020295708 0.97352278690986982
## 155          race_final_label -0.03100524184 0.96535916817740675
## 156          race_final_label -0.43936740430 0.38636569597660375
## 157          race_final_label -0.01042341464 0.98576358357417404
## 158          race_final_label -0.11707470105 0.77822396547545969
## 159          race_final_label -0.11828239505 0.30947290327306004
## 160          race_final_label  1.41533588799 0.16063322156113585
## 161          race_final_label -0.23230174880 0.43522678347209232
## 162          race_final_label -0.25018833673 0.51661750631772252
## 163                 ethnicity  0.19057081425 0.43185377958703053
## 164                 ethnicity  0.01020295708 0.97349654239087935
## 165                     rural -0.16657258549 0.37123860525779162
## 166                     rural -0.12227732775 0.45397511461727769
## 167                   smoking -0.55049716636 0.00222769302932113
## 168                   smoking -0.24303361665 0.03579987987392344
## 169          sq_drink_alcohol -0.15214353078 0.32289373258876941
## 170          sq_drink_alcohol -0.06457873589 0.65051917779037560
## 171          sq_drink_alcohol  0.39510198704 0.00571563030051612
## 172  sq_average_drink_per_day -0.17936334767 0.48139369719176539
## 173  sq_average_drink_per_day  0.33975607179 0.41243345212405735
## 174  sq_average_drink_per_day -0.47566543041 0.00012072205421371
## 175             sq_self_hep_b -0.16998081239 0.13479409364303724
## 176             sq_self_hep_b  0.07765272744 0.75677422534221872
## 177             sq_self_hep_c -0.15324170559 0.17920855998162405
## 178             sq_self_hep_c -0.04152663629 0.86504317800143804
## 179         supp_meds_tylenol  0.13783174590 0.71761513785548414
## 180         supp_meds_tylenol  1.04553009228 0.09550981098712773
## 181        supp_meds_steroids -0.30899292899 0.36097388049451384
## 182        supp_meds_steroids  0.10145385531 0.92347692411988780
## 183             sq_water_well -0.19791095634 0.07794860503604970
## 184             sq_water_well -0.14505144638 0.34325528210841694
## 185   sq_water_tap_unfiltered -0.06685848275 0.63379349809383023
## 186   sq_water_tap_unfiltered  0.07774682125 0.56054814450977120
## 187 sq_water_house_filtration -0.15719663895 0.15011332667990274
## 188 sq_water_house_filtration -0.06359736630 0.72917960795943682
## 189    sq_water_faucet_filter -0.16765748230 0.16701503357658093
## 190    sq_water_faucet_filter -0.06287281614 0.63038370929637866
## 191  sq_water_charcoal_filter -0.17148290513 0.12453939373143023
## 192  sq_water_charcoal_filter -0.08827373415 0.57281724091754371
## 193          sq_water_bottled -0.31695071374 0.02481255320482565
## 194          sq_water_bottled -0.25741602293 0.05871655083207015
## 195             sq_water_none -0.15039524262 0.15986807660112737
## 196             sq_water_none -0.28059297095 0.25347435646347488
## 197       sq_water_other_type -0.17879316843 0.09958577804326338
## 198       sq_water_other_type -0.06326299693 0.75719660380250520
## 199                    source -0.02812398302 0.85071911056610372
## 200                    source  0.06545950319 0.60757504689611586
## 201                    source -0.54934387889 0.03293039529831022
## 202                       sex  0.17018018712 0.11421477651813232
## 203            race_eth_label  0.17735142739 0.48947961492581005
## 204            race_eth_label  0.21368881066 0.54222117666899239
## 205            race_eth_label  0.22072926125 0.36962368798292344
## 206            race_eth_label -0.06425467746 0.83445298522020916
## 207          race_final_label -0.25650160083 0.71719909410729121
## 208          race_final_label -0.61212402402 0.22393436309559747
## 209          race_final_label  0.30221826119 0.60200810793987869
## 210          race_final_label  0.34506062946 0.40285038365018688
## 211          race_final_label -0.03033696946 0.79256603065346942
## 212          race_final_label  2.50633119704 0.01250591825764212
## 213          race_final_label -0.26150115907 0.37590551468124966
## 214          race_final_label -0.35693228717 0.35102751784317132
## 215                 ethnicity  0.20729582419 0.39191644876826492
## 216                 ethnicity -0.06425467746 0.83404410390462613
## 217                     rural -0.15050379014 0.41945767850033333
## 218                     rural -0.02567749874 0.87510243365735252
## 219                   smoking -0.56816147223 0.00162483462451516
## 220                   smoking -0.17764283744 0.12484388703392600
## 221          sq_drink_alcohol -0.09279790313 0.55221901412799523
## 222          sq_drink_alcohol -0.03617257237 0.80250707464714910
## 223          sq_drink_alcohol  0.25098982175 0.08264764890083667
## 224  sq_average_drink_per_day -0.14733130408 0.56878498930871513
## 225  sq_average_drink_per_day  0.13741276657 0.74388410618383627
## 226  sq_average_drink_per_day -0.30905433609 0.01330787645466807
## 227             sq_self_hep_b -0.09608172406 0.39765419685309278
## 228             sq_self_hep_b  0.27321564105 0.27638203097614938
## 229             sq_self_hep_c -0.08827848999 0.43852931773111470
## 230             sq_self_hep_c  0.26631945107 0.27592232776750170
## 231         supp_meds_tylenol  0.22225409868 0.56129273508638611
## 232         supp_meds_tylenol  0.49018745604 0.43550301353660792
## 233        supp_meds_steroids -0.00137046605 0.99676971448586982
## 234        supp_meds_steroids -0.08300121298 0.93744563737665432
## 235             sq_water_well -0.13821435834 0.21869101547986863
## 236             sq_water_well -0.14605474755 0.34083898263584533
## 237   sq_water_tap_unfiltered -0.10884128949 0.43862900238366409
## 238   sq_water_tap_unfiltered -0.03152028087 0.81362118915627635
## 239 sq_water_house_filtration -0.10144195077 0.35332742478950041
## 240 sq_water_house_filtration  0.02881244426 0.87552600125445079
## 241    sq_water_faucet_filter -0.14451134890 0.23371706850434837
## 242    sq_water_faucet_filter -0.11576443844 0.37605746039474852
## 243  sq_water_charcoal_filter -0.09435771005 0.39811618261251014
## 244  sq_water_charcoal_filter  0.07837485262 0.61705027014982561
## 245          sq_water_bottled -0.37438959384 0.00776080774017603
## 246          sq_water_bottled -0.39800413798 0.00339692534565435
## 247             sq_water_none -0.09715229470 0.36444330562043414
## 248             sq_water_none -0.22134405610 0.36842362123646244
## 249       sq_water_other_type -0.11865182176 0.27482381292786012
## 250       sq_water_other_type -0.12858001415 0.53059508137585887
## 251                    source  0.10434243584 0.48142224323953187
## 252                    source -0.25317070261 0.04569477728082912
## 253                    source  0.36671855653 0.15030326977519787
## 254                       sex  0.04753972206 0.65952777755572933
## 255            race_eth_label -0.29217269122 0.24558725047749871
## 256            race_eth_label  0.25904064681 0.45098650537993790
## 257            race_eth_label  0.19739330324 0.41292192888074941
## 258            race_eth_label -0.00442983508 0.98826938151728350
## 259          race_final_label  2.99035334157 0.00001177078990472
## 260          race_final_label -0.75076319456 0.11711937574190467
## 261          race_final_label -0.21362555587 0.69833970735643969
## 262          race_final_label -0.20106093275 0.60825038534995124
## 263          race_final_label -0.52810627333 0.00000214067537662
## 264          race_final_label  0.14446620325 0.87920452393557480
## 265          race_final_label -0.54187633507 0.05422224390061022
## 266          race_final_label -0.64172021528 0.07841348875071134
## 267                 ethnicity  0.05211948502 0.82999524103981692
## 268                 ethnicity -0.00442983508 0.98850968884498225
## 269                     rural  0.03981842586 0.83089199323123042
## 270                     rural  0.06732367912 0.68047191960674125
## 271                   smoking -0.25860857221 0.15406242068696158
## 272                   smoking -0.04266261101 0.71521597801003944
## 273          sq_drink_alcohol  0.13586280492 0.38818440115655084
## 274          sq_drink_alcohol  0.04809961613 0.74153340009344393
## 275          sq_drink_alcohol  0.06560293585 0.65217578709575408
## 276  sq_average_drink_per_day  0.34358261099 0.18358567488178576
## 277  sq_average_drink_per_day  1.13305976415 0.00724023699402287
## 278  sq_average_drink_per_day  0.11291955180 0.36326518486072301
## 279             sq_self_hep_b -0.00524517482 0.96324302106208015
## 280             sq_self_hep_b -0.15705182864 0.53229219046349652
## 281             sq_self_hep_c  0.07666664632 0.50144498984545860
## 282             sq_self_hep_c  0.25616463159 0.29506967023341613
## 283         supp_meds_tylenol  0.14236624332 0.70995004114878091
## 284         supp_meds_tylenol  0.09558088307 0.87917492730682845
## 285        supp_meds_steroids  0.10616093523 0.75379520356562613
## 286        supp_meds_steroids -0.00628231156 0.99525978948954863
## 287             sq_water_well -0.07901703786 0.48242764822962969
## 288             sq_water_well -0.02984035320 0.84588876056705242
## 289   sq_water_tap_unfiltered  0.03916307547 0.78055819320376185
## 290   sq_water_tap_unfiltered  0.05959691935 0.65602092727267491
## 291 sq_water_house_filtration -0.06483291389 0.55318487403520389
## 292 sq_water_house_filtration  0.04468035213 0.80820617839498143
## 293    sq_water_faucet_filter  0.04534724923 0.70845488436320547
## 294    sq_water_faucet_filter  0.15306167211 0.24212063939652043
## 295  sq_water_charcoal_filter -0.08703304619 0.43594263519054810
## 296  sq_water_charcoal_filter -0.13294540628 0.39671140672906335
## 297          sq_water_bottled -0.16259904098 0.25016970657882531
## 298          sq_water_bottled -0.21830266548 0.10999177430940106
## 299             sq_water_none -0.05212974655 0.62687470346804519
## 300             sq_water_none  0.04128376170 0.86689577335487455
## 301       sq_water_other_type -0.08433147055 0.43780014503922282
## 302       sq_water_other_type  0.05985138573 0.77047252115801956
## 303                    source -0.24209283532 0.10550138874091017
## 304                    source -0.31476257326 0.01381394218896274
## 305                    source -0.43870902249 0.08785424567301257
## 306                       sex -0.07419888249 0.49160923227274667
## 307            race_eth_label -0.01464216199 0.95432754792565500
## 308            race_eth_label -0.02338660368 0.94662591167007082
## 309            race_eth_label  0.21620594043 0.37788131612548015
## 310            race_eth_label  0.29446925111 0.33681006421286097
## 311          race_final_label -0.06497780823 0.92729153707792866
## 312          race_final_label -0.36498230103 0.47055950006093461
## 313          race_final_label -0.28720334611 0.62210940950523153
## 314          race_final_label -0.23482160095 0.57120995522040818
## 315          race_final_label -0.26151753168 0.02465261727798186
## 316          race_final_label -0.32212450954 0.74855511160559651
## 317          race_final_label -0.27609206683 0.35256225658706697
## 318          race_final_label -0.33436959282 0.38492197181345200
## 319                 ethnicity  0.13563843592 0.57595190803645446
## 320                 ethnicity  0.29446925111 0.33814572280041677
## 321                     rural  0.40223577490 0.03046477151052450
## 322                     rural -0.02546298067 0.87544973896637868
## 323                   smoking  0.02876208111 0.87397920554472974
## 324                   smoking -0.13222001617 0.25890118865789558
## 325          sq_drink_alcohol  0.03093608928 0.84399176612941307
## 326          sq_drink_alcohol -0.11354395865 0.43588771007606608
## 327          sq_drink_alcohol  0.04248949045 0.77007949378540197
## 328  sq_average_drink_per_day  0.00173849948 0.99459668702331505
## 329  sq_average_drink_per_day  1.43403530887 0.00065732225553094
## 330  sq_average_drink_per_day  0.00140892991 0.99089640548374591
## 331             sq_self_hep_b -0.16164730427 0.15490547435844459
## 332             sq_self_hep_b -0.23109631067 0.35688641562230339
## 333             sq_self_hep_c -0.11062049213 0.33226578512771388
## 334             sq_self_hep_c  0.11736859792 0.63123268162752577
## 335         supp_meds_tylenol  0.05111422485 0.89376908860933635
## 336         supp_meds_tylenol -0.10000151347 0.87363945925546149
## 337        supp_meds_steroids  0.06764799759 0.84159227516948099
## 338        supp_meds_steroids -0.13545178545 0.89808056927624569
## 339             sq_water_well  0.02866859932 0.79878534040690352
## 340             sq_water_well -0.08368273867 0.58576338919294979
## 341   sq_water_tap_unfiltered -0.04878699378 0.72768354490860843
## 342   sq_water_tap_unfiltered -0.19626578122 0.14160342074544699
## 343 sq_water_house_filtration  0.06616698617 0.54472537125352871
## 344 sq_water_house_filtration  0.18374184953 0.31807308447128430
## 345    sq_water_faucet_filter  0.08488801129 0.48456664213754364
## 346    sq_water_faucet_filter  0.05917333707 0.65126163583327279
## 347  sq_water_charcoal_filter  0.06196009253 0.57934760076694691
## 348  sq_water_charcoal_filter  0.08690500377 0.57974234090501586
## 349          sq_water_bottled  0.00150677405 0.99150577538440487
## 350          sq_water_bottled -0.11319237198 0.40754821004133213
## 351             sq_water_none  0.03361244212 0.75393115719870929
## 352             sq_water_none -0.08365917336 0.73415922696710800
## 353       sq_water_other_type  0.02430377718 0.82315529229054696
## 354       sq_water_other_type -0.04733327578 0.81768277140597645
## 355                    source -0.07628426924 0.61053968426051308
## 356                    source  0.06932016021 0.58737751970846286
## 357                    source -0.42856769023 0.09643431102686301
## 358                       sex -0.12516480332 0.24571033496867928
## 359            race_eth_label -0.14944647943 0.55373366719946415
## 360            race_eth_label  0.13817704270 0.68861553399694753
## 361            race_eth_label  0.29883181637 0.21707705491636420
## 362            race_eth_label  0.01456195611 0.96159120427070899
## 363          race_final_label  0.23361134664 0.73943226114684735
## 364          race_final_label -0.68257158805 0.17165331000666206
## 365          race_final_label  0.06701195493 0.90716829723055903
## 366          race_final_label  0.01299508731 0.97464817278574101
## 367          race_final_label -0.43096670466 0.00019045927418025
## 368          race_final_label  0.54236038999 0.58429976766167235
## 369          race_final_label -0.30030274488 0.30534074418752660
## 370          race_final_label  0.02574794177 0.94588993724285442
## 371                 ethnicity  0.15605957242 0.51999177754676029
## 372                 ethnicity  0.01456195611 0.96220473628700909
## 373                     rural -0.19014848628 0.30702073717271200
## 374                     rural -0.17063001679 0.29578828788986516
## 375                   smoking -0.40978883374 0.02321464375482415
## 376                   smoking -0.22263200726 0.05574471466387071
## 377          sq_drink_alcohol -0.11149608945 0.46913030842951009
## 378          sq_drink_alcohol -0.04673299431 0.74326861358173835
## 379          sq_drink_alcohol  0.40955746795 0.00422640049175355
## 380  sq_average_drink_per_day -0.04042465331 0.87408657104043130
## 381  sq_average_drink_per_day -0.26009324830 0.53096833080426087
## 382  sq_average_drink_per_day -0.48147462903 0.00010257804469636
## 383             sq_self_hep_b -0.17885175294 0.11559742974387974
## 384             sq_self_hep_b  0.04522819244 0.85681327359605108
## 385             sq_self_hep_c -0.17786660733 0.11880220685086884
## 386             sq_self_hep_c  0.00515648437 0.98314670068817211
## 387         supp_meds_tylenol  0.44380109830 0.24563690847941486
## 388         supp_meds_tylenol  0.77050797170 0.21985255079337265
## 389        supp_meds_steroids  0.10780508159 0.75003621633080930
## 390        supp_meds_steroids -0.38080944008 0.71869901424882265
## 391             sq_water_well -0.18653694538 0.09675928899760877
## 392             sq_water_well -0.11223939809 0.46350632851723095
## 393   sq_water_tap_unfiltered -0.00717499280 0.95913876245625251
## 394   sq_water_tap_unfiltered  0.17549299403 0.18845245498274266
## 395 sq_water_house_filtration -0.16064803523 0.14111864812130245
## 396 sq_water_house_filtration -0.18414678866 0.31607063806374974
## 397    sq_water_faucet_filter -0.18099109271 0.13559656249832411
## 398    sq_water_faucet_filter -0.14435720063 0.26925087582704033
## 399  sq_water_charcoal_filter -0.17126417939 0.12499948414919104
## 400  sq_water_charcoal_filter -0.04422335533 0.77750970889267790
## 401          sq_water_bottled -0.43728254052 0.00182086074126584
## 402          sq_water_bottled -0.45962196552 0.00069540024129137
## 403             sq_water_none -0.16543176316 0.12150146864343667
## 404             sq_water_none -0.39284391304 0.10950059241961881
## 405       sq_water_other_type -0.18103712113 0.09537905447063832
## 406       sq_water_other_type -0.03766610528 0.85393082686462518
## 407                    source -0.59790103757 0.00005228868616012
## 408                    source -0.26557025946 0.03370576474755375
## 409                    source  0.32661198117 0.19403427471930992
## 410                       sex -0.15368685249 0.15386599131093590
## 411            race_eth_label  0.07231545684 0.77714097293777329
## 412            race_eth_label  0.47425865769 0.17483025510465880
## 413            race_eth_label  0.08698417548 0.72246058781372169
## 414            race_eth_label  0.45767860326 0.13554981673753960
## 415          race_final_label  0.34729696079 0.62303943070382883
## 416          race_final_label  1.65297624760 0.00107063992871887
## 417          race_final_label -0.07238566712 0.90034178273734011
## 418          race_final_label -0.04129806505 0.92002234501068081
## 419          race_final_label  0.06298670916 0.58422548881022029
## 420          race_final_label -0.25891127952 0.79509259208137861
## 421          race_final_label -0.20709863531 0.48201712804660735
## 422          race_final_label -0.22782367745 0.55058320024782048
## 423                 ethnicity  0.09994099180 0.67927159271312165
## 424                 ethnicity  0.45767860326 0.13566211156981897
## 425                     rural -0.14723144214 0.42822635251511909
## 426                     rural -0.26072761706 0.11000532282995749
## 427                   smoking  0.21611201277 0.23338823688726526
## 428                   smoking -0.06618776224 0.57137978178831528
## 429          sq_drink_alcohol  0.10156874252 0.51822776011725313
## 430          sq_drink_alcohol -0.00190253764 0.98957692131135044
## 431          sq_drink_alcohol  0.15143861403 0.29776677122813072
## 432  sq_average_drink_per_day  0.05748739112 0.82519940509489453
## 433  sq_average_drink_per_day -0.34492434674 0.41554963544339829
## 434  sq_average_drink_per_day -0.13243173333 0.29051139953655003
## 435             sq_self_hep_b -0.12772680791 0.26138986317014279
## 436             sq_self_hep_b -0.18926614933 0.45099558920554317
## 437             sq_self_hep_c -0.12436403200 0.27562127705553680
## 438             sq_self_hep_c -0.19167028106 0.43308498300754394
## 439         supp_meds_tylenol  0.25950328321 0.49765332661943340
## 440         supp_meds_tylenol  0.07771900517 0.90156876480204828
## 441        supp_meds_steroids  0.21785483823 0.51959775927009866
## 442        supp_meds_steroids -0.29015095263 0.78364104048900063
## 443             sq_water_well -0.03539646609 0.75308976935880034
## 444             sq_water_well -0.00403742287 0.97903005717147185
## 445   sq_water_tap_unfiltered -0.22763959852 0.10460564698569111
## 446   sq_water_tap_unfiltered -0.20965868166 0.11625097875513281
## 447 sq_water_house_filtration  0.03180003008 0.76900375265055310
## 448 sq_water_house_filtration  0.49963711198 0.00639538693209372
## 449    sq_water_faucet_filter -0.00731918832 0.95193128359767076
## 450    sq_water_faucet_filter  0.07300597137 0.57712410414268367
## 451  sq_water_charcoal_filter -0.02645028662 0.81297502605540750
## 452  sq_water_charcoal_filter  0.03855810651 0.80596269263470566
## 453          sq_water_bottled -0.12807664111 0.36598943670439332
## 454          sq_water_bottled -0.10002749761 0.46439951786494849
## 455             sq_water_none -0.00134633678 0.98995994083384609
## 456             sq_water_none -0.33307110634 0.17591982687334426
## 457       sq_water_other_type  0.02991785344 0.78311655900996846
## 458       sq_water_other_type  0.14932828872 0.46695697882131204
## 459                    source -0.12233567491 0.40741994614468358
## 460                    source  0.16665102186 0.18606871857653695
## 461                    source -0.71085759563 0.00529967763418086
## 462                       sex -0.08107283790 0.45234435016075591
## 463            race_eth_label  0.38843641214 0.12719466758624104
## 464            race_eth_label  0.75496304658 0.03031047612559701
## 465            race_eth_label  0.30938744827 0.20479634375196532
## 466            race_eth_label -0.06964204857 0.81923914578153800
## 467          race_final_label -0.54217003211 0.43999328372661040
## 468          race_final_label -0.49473597649 0.32117620165951011
## 469          race_final_label  0.31984176703 0.57762843834789201
## 470          race_final_label  1.39032924586 0.00073512345398631
## 471          race_final_label  0.08140436173 0.47660773266544987
## 472          race_final_label  0.53300229345 0.59053852024600917
## 473          race_final_label -0.22594287753 0.44010936421997648
## 474          race_final_label -0.56024015523 0.14005245576397632
## 475                 ethnicity  0.35330235643 0.14289994452075006
## 476                 ethnicity -0.06964204857 0.81947623415181903
## 477                     rural -0.21079073860 0.25793964453798679
## 478                     rural -0.04719442936 0.77247965322541445
## 479                   smoking -0.55938000641 0.00181874007453686
## 480                   smoking -0.32017764170 0.00560773476027302
## 481          sq_drink_alcohol -0.17786232689 0.24777442714207512
## 482          sq_drink_alcohol -0.16663937622 0.24258750496615397
## 483          sq_drink_alcohol  0.34004163394 0.01719459843915620
## 484  sq_average_drink_per_day  0.16709286937 0.51266621906039722
## 485  sq_average_drink_per_day  0.03513635302 0.93254748917459640
## 486  sq_average_drink_per_day -0.42766182037 0.00054676111079222
## 487             sq_self_hep_b -0.22785512960 0.04436660508739628
## 488             sq_self_hep_b  0.24387552625 0.32895313970867956
## 489             sq_self_hep_c -0.23110609461 0.04207291408647388
## 490             sq_self_hep_c  0.22064001249 0.36434293822168251
## 491         supp_meds_tylenol -0.32443230158 0.39565811530909123
## 492         supp_meds_tylenol  0.26527944634 0.67233383417726300
## 493        supp_meds_steroids -0.43690149238 0.19622409137501412
## 494        supp_meds_steroids -0.02186261809 0.98346705424688374
## 495             sq_water_well -0.12817120720 0.25427262322351152
## 496             sq_water_well -0.02495098293 0.87074988658916408
## 497   sq_water_tap_unfiltered -0.22205510583 0.11348990546404761
## 498   sq_water_tap_unfiltered -0.07001753613 0.59948647097832886
## 499 sq_water_house_filtration -0.13263834075 0.22481218952720550
## 500 sq_water_house_filtration -0.03978205473 0.82866854646978583
## 501    sq_water_faucet_filter -0.13144880015 0.27842603953196615
## 502    sq_water_faucet_filter  0.02976012029 0.81982321014451420
## 503  sq_water_charcoal_filter -0.11479817257 0.30392858519815302
## 504  sq_water_charcoal_filter  0.05286413942 0.73582611255727159
## 505          sq_water_bottled -0.35114193595 0.01283108050418380
## 506          sq_water_bottled -0.28139780657 0.03857335714816601
## 507             sq_water_none -0.09645572879 0.36794161779746204
## 508             sq_water_none -0.20912526088 0.39547290349843378
## 509       sq_water_other_type -0.13920849554 0.20007125981871401
## 510       sq_water_other_type -0.07811881114 0.70304722582391288
## 511                    source  0.06168004872 0.67844612713381902
## 512                    source  0.22832573243 0.07252736028874485
## 513                    source -0.45648133987 0.07474208092999160
## 514                       sex  0.30350051034 0.00469493718683770
## 515            race_eth_label  0.17144822696 0.50197497862570550
## 516            race_eth_label -0.08286614721 0.81223970154000491
## 517            race_eth_label  0.35914307931 0.14277455985685836
## 518            race_eth_label  0.17094081549 0.57643705503039200
## 519          race_final_label -0.35595703100 0.61712705883957342
## 520          race_final_label -0.75988071120 0.13339352193114282
## 521          race_final_label -0.06325872262 0.91352122741302155
## 522          race_final_label -0.37547025156 0.36528543014309145
## 523          race_final_label -0.16785923828 0.14835335109303602
## 524          race_final_label  1.00801708607 0.31601277293424263
## 525          race_final_label -0.25448828409 0.39135877967638943
## 526          race_final_label -0.27232894289 0.47898862867843928
## 527                 ethnicity  0.28253434133 0.24394937568137418
## 528                 ethnicity  0.17094081549 0.57772753504935903
## 529                     rural -0.15330859096 0.41080517756177970
## 530                     rural -0.05287271038 0.74619014229260427
## 531                   smoking -0.59743152776 0.00092276549007707
## 532                   smoking -0.12179225914 0.29205421769068646
## 533          sq_drink_alcohol -0.06535445769 0.67660542380994215
## 534          sq_drink_alcohol  0.01175053534 0.93549031572167918
## 535          sq_drink_alcohol  0.20806866087 0.15152091369328727
## 536  sq_average_drink_per_day  0.01595902681 0.95098333148843639
## 537  sq_average_drink_per_day  0.06376641718 0.88000354530749192
## 538  sq_average_drink_per_day -0.21608972737 0.08417452030592833
## 539             sq_self_hep_b -0.02247278890 0.84347580470059136
## 540             sq_self_hep_b  0.14660302112 0.55989141502025297
## 541             sq_self_hep_c -0.02853443225 0.80272136125276106
## 542             sq_self_hep_c  0.02671288685 0.91313950657492171
## 543         supp_meds_tylenol  0.30269349191 0.42836384684072104
## 544         supp_meds_tylenol  0.78143889628 0.21353816457676289
## 545        supp_meds_steroids -0.10559143425 0.75503614022516952
## 546        supp_meds_steroids -0.46625437338 0.65925938965952646
## 547             sq_water_well -0.06777253946 0.54651262733604811
## 548             sq_water_well -0.15540013737 0.31136152736212841
## 549   sq_water_tap_unfiltered -0.00727570282 0.95872659269934801
## 550   sq_water_tap_unfiltered  0.02603227415 0.84574417416999270
## 551 sq_water_house_filtration -0.03605705995 0.74164992928474538
## 552 sq_water_house_filtration -0.01642026493 0.92895252444544685
## 553    sq_water_faucet_filter -0.07838889488 0.51835547757238953
## 554    sq_water_faucet_filter -0.12496215620 0.33973975540600310
## 555  sq_water_charcoal_filter -0.04281037697 0.70176869616976578
## 556  sq_water_charcoal_filter  0.02425610311 0.87718241871528879
## 557          sq_water_bottled -0.34343405338 0.01441692733043675
## 558          sq_water_bottled -0.43313180106 0.00143238137611300
## 559             sq_water_none -0.03958620288 0.71147423698221934
## 560             sq_water_none -0.32045363493 0.19292650115117382
## 561       sq_water_other_type -0.04449232868 0.68223041086397185
## 562       sq_water_other_type -0.16656898382 0.41705478475211033
## 563                    source  0.00341597575 0.98179146397574657
## 564                    source  0.04502069166 0.72434393181777690
## 565                    source -0.51200370343 0.04708117779278184
## 566                       sex -0.02359390772 0.82693773382376023
## 567            race_eth_label  0.06086368697 0.81187103175202535
## 568            race_eth_label  0.28356857907 0.41725220517750161
## 569            race_eth_label  0.17080174030 0.48604179615837562
## 570            race_eth_label -0.23994111834 0.43384263785675281
## 571          race_final_label -0.46259942334 0.51535911876491247
## 572          race_final_label -0.48699606048 0.33501616397590472
## 573          race_final_label  0.00662795737 0.99090900750855360
## 574          race_final_label  0.76233095269 0.06613992103282181
## 575          race_final_label -0.10757306829 0.35324429414612923
## 576          race_final_label -0.15142758139 0.88002788778056229
## 577          race_final_label -0.07631609842 0.79678214569844263
## 578          race_final_label -0.54040796213 0.15992207734304267
## 579                 ethnicity  0.14262137861 0.55500838863838109
## 580                 ethnicity -0.23994111834 0.43331808561261076
## 581                     rural -0.13286357000 0.47604978557855959
## 582                     rural  0.00406386978 0.98015694799679176
## 583                   smoking -0.33925333501 0.06089981157603477
## 584                   smoking -0.16043150025 0.16912075431887635
## 585          sq_drink_alcohol  0.03369684146 0.82844187593956276
## 586          sq_drink_alcohol  0.03928903694 0.78511449567257685
## 587          sq_drink_alcohol  0.37799051426 0.00887878150753327
## 588  sq_average_drink_per_day -0.14649866148 0.56947537328377917
## 589  sq_average_drink_per_day -0.00615415023 0.98827941703331035
## 590  sq_average_drink_per_day -0.37744411216 0.00245565678435985
## 591             sq_self_hep_b -0.14105158765 0.21493465638586645
## 592             sq_self_hep_b  0.04210692449 0.86677177612829825
## 593             sq_self_hep_c -0.09979353088 0.37976264387495307
## 594             sq_self_hep_c  0.42840676793 0.07911871955041586
## 595         supp_meds_tylenol -0.24174331541 0.52552310304422201
## 596         supp_meds_tylenol  0.74300215321 0.23522327501678239
## 597        supp_meds_steroids -0.21852365905 0.51797858027497301
## 598        supp_meds_steroids  0.71454274816 0.49868186690490035
## 599             sq_water_well  0.01168605994 0.91721458430984049
## 600             sq_water_well  0.12608602047 0.41156166219780010
## 601   sq_water_tap_unfiltered -0.09220049374 0.51184853848187739
## 602   sq_water_tap_unfiltered -0.07543539858 0.57279182355582148
## 603 sq_water_house_filtration -0.01192867507 0.91310090818639877
## 604 sq_water_house_filtration -0.12977075568 0.48093371950682218
## 605    sq_water_faucet_filter -0.02728505772 0.82219076468470564
## 606    sq_water_faucet_filter -0.09022857915 0.49076539977288947
## 607  sq_water_charcoal_filter  0.03682718974 0.74186846476577573
## 608  sq_water_charcoal_filter  0.04040313633 0.79688719547918652
## 609          sq_water_bottled -0.17773609857 0.20792533592251639
## 610          sq_water_bottled -0.27538320757 0.04357849187013037
## 611             sq_water_none  0.02229770786 0.83524455521134733
## 612             sq_water_none -0.12918736057 0.59997849432490002
## 613       sq_water_other_type -0.06032328779 0.57889504928244273
## 614       sq_water_other_type  0.09236555500 0.65262891764171804
## 615                    source  0.12846146544 0.38687468700134564
## 616                    source  0.39720909646 0.00181891699048696
## 617                    source  0.04155554741 0.87053458291733876
## 618                       sex  0.08119651991 0.45165441374253601
## 619            race_eth_label -0.00586181941 0.98166500714231375
## 620            race_eth_label  0.07238360316 0.83548591495916558
## 621            race_eth_label  0.27902406267 0.25418843077719411
## 622            race_eth_label  0.04716044722 0.87739425723636211
## 623          race_final_label  1.36028732641 0.05400990074290221
## 624          race_final_label -0.67792247840 0.17574960546629939
## 625          race_final_label -0.05066729394 0.92993187406726230
## 626          race_final_label -0.43348449368 0.29078243295853851
## 627          race_final_label -0.30480826458 0.00818424840147474
## 628          race_final_label  0.21183589124 0.83120895797991856
## 629          race_final_label -0.36639427441 0.21245914434554627
## 630          race_final_label -0.37159837289 0.32895717410028713
## 631                 ethnicity  0.18359567650 0.44910052978292048
## 632                 ethnicity  0.04716044722 0.87801175679233379
## 633                     rural -0.29369835283 0.11397432465805926
## 634                     rural -0.22595188987 0.16528201759409997
## 635                   smoking -0.32812133861 0.07025007004597797
## 636                   smoking -0.08647518574 0.45886310938747255
## 637          sq_drink_alcohol  0.00872138962 0.95557605731020900
## 638          sq_drink_alcohol  0.05329752766 0.71336276014425226
## 639          sq_drink_alcohol  0.26418668784 0.06864932704252635
## 640  sq_average_drink_per_day  0.34598625878 0.18167914444426966
## 641  sq_average_drink_per_day -0.07706509184 0.85474769829995156
## 642  sq_average_drink_per_day -0.18716258000 0.13320907616193359
## 643             sq_self_hep_b -0.04384126872 0.70010452722554173
## 644             sq_self_hep_b -0.14852356441 0.55475236942286110
## 645             sq_self_hep_c -0.00850483650 0.94062361844988573
## 646             sq_self_hep_c -0.13325302642 0.58632012495494368
## 647         supp_meds_tylenol  0.18150755550 0.63535705748587723
## 648         supp_meds_tylenol  0.16741359119 0.79002340503005719
## 649        supp_meds_steroids  0.16256745203 0.63085743718607123
## 650        supp_meds_steroids -0.40299902397 0.70298916766313346
## 651             sq_water_well -0.03177598964 0.77734871293318042
## 652             sq_water_well -0.16152694531 0.29269446086609541
## 653   sq_water_tap_unfiltered  0.13088726281 0.34954163790135262
## 654   sq_water_tap_unfiltered  0.26544081020 0.04667168825859383
## 655 sq_water_house_filtration  0.03539716070 0.74609488302110494
## 656 sq_water_house_filtration  0.12286695845 0.50458013295510473
## 657    sq_water_faucet_filter  0.07422498204 0.54112145438894954
## 658    sq_water_faucet_filter  0.04600188469 0.72532178061484442
## 659  sq_water_charcoal_filter -0.01701590491 0.87904866822130390
## 660  sq_water_charcoal_filter -0.00714787774 0.96368572588700163
## 661          sq_water_bottled -0.30337952011 0.02943808106098152
## 662          sq_water_bottled -0.52713535708 0.00009910794049467
## 663             sq_water_none  0.03220906487 0.76347440250852572
## 664             sq_water_none -0.28637917545 0.24456352714546090
## 665       sq_water_other_type -0.00069820403 0.99487761581831213
## 666       sq_water_other_type  0.04767553387 0.81640595739469646
## 667                    source  0.51882522422 0.00035296753016782
## 668                    source  0.27329328029 0.02652537691163277
## 669                    source  1.36562416595 0.00000006218287011
## 670                       sex -0.15256425529 0.15690319949754505
## 671            race_eth_label -0.09961913738 0.69832721067772541
## 672            race_eth_label -0.28068732935 0.42434022335421961
## 673            race_eth_label -0.15502571296 0.52926880834615642
## 674            race_eth_label -0.24017983164 0.43570815119399686
## 675          race_final_label  0.36314576107 0.60936886944372626
## 676          race_final_label -0.12549586113 0.80359047025394681
## 677          race_final_label  0.08595421817 0.88247237807532541
## 678          race_final_label -0.55771131397 0.17818156233011151
## 679          race_final_label  0.05714150773 0.62161421926663007
## 680          race_final_label  2.15432621489 0.03221506802430414
## 681          race_final_label -0.13260894822 0.65440808798253147
## 682          race_final_label  0.32256034411 0.40092858744280124
## 683                 ethnicity -0.14391451569 0.55309406174542874
## 684                 ethnicity -0.24017983164 0.43478718661802029
## 685                     rural -0.03072806294 0.86877802201807885
## 686                     rural  0.22724981485 0.16396371383201136
## 687                   smoking  0.38382346020 0.03156290235866580
## 688                   smoking  0.42443224230 0.00024963802756887
## 689          sq_drink_alcohol -0.10651007576 0.49296709044419185
## 690          sq_drink_alcohol  0.29694661582 0.03963369285390450
## 691          sq_drink_alcohol -0.08549871620 0.55172036530077662
## 692  sq_average_drink_per_day -0.10442762134 0.68793945814009794
## 693  sq_average_drink_per_day -0.27319094465 0.51845922867124128
## 694  sq_average_drink_per_day  0.13982610455 0.26388573205592636
## 695             sq_self_hep_b  0.33867951731 0.00270288931958967
## 696             sq_self_hep_b -0.23822011787 0.33688740607011625
## 697             sq_self_hep_c  0.38630395960 0.00064482875448686
## 698             sq_self_hep_c  0.33973550067 0.15897129077815536
## 699         supp_meds_tylenol -0.03979105231 0.91717244608161719
## 700         supp_meds_tylenol -0.35438704003 0.57295049357703465
## 701        supp_meds_steroids  0.17348130782 0.60813457682162686
## 702        supp_meds_steroids  0.65707774780 0.53424295732899529
## 703             sq_water_well  0.28458588928 0.01091883956148439
## 704             sq_water_well -0.05692071246 0.70803138909271612
## 705   sq_water_tap_unfiltered  0.37268296635 0.00767897644492330
## 706   sq_water_tap_unfiltered  0.13244034482 0.31755980881213985
## 707 sq_water_house_filtration  0.27652632056 0.01100469577269840
## 708 sq_water_house_filtration -0.06234680346 0.73243980970183031
## 709    sq_water_faucet_filter  0.33122239842 0.00611511609349293
## 710    sq_water_faucet_filter  0.09641517113 0.45709065759293344
## 711  sq_water_charcoal_filter  0.28947527111 0.00910866204875724
## 712  sq_water_charcoal_filter -0.08575238784 0.58051667684539865
## 713          sq_water_bottled  0.32964303863 0.01932347445562221
## 714          sq_water_bottled  0.06993004302 0.60590779765292924
## 715             sq_water_none  0.27947592083 0.00867219569146533
## 716             sq_water_none -0.22926941595 0.34669199788674454
## 717       sq_water_other_type  0.33721476837 0.00169010311525126
## 718       sq_water_other_type  0.56525519790 0.00524748730281680
## 719                    source -0.50659899172 0.00049247622953313
## 720                    source -0.71331858138 0.00000001380182137
## 721                    source -0.67687749138 0.00655977315886617
## 722                       sex  0.31907869439 0.00293897166877725
## 723            race_eth_label -0.38456128103 0.13013569457742086
## 724            race_eth_label -0.01545994530 0.96442794455498793
## 725            race_eth_label -0.17734261188 0.46598140195591820
## 726            race_eth_label  0.28233582862 0.35335867587746195
## 727          race_final_label  1.13561169417 0.10806768285307439
## 728          race_final_label -0.42530906633 0.39610942244147207
## 729          race_final_label -0.47144851746 0.41428300467104739
## 730          race_final_label  0.30627546779 0.45604501729654012
## 731          race_final_label -0.30743253810 0.00776078864512350
## 732          race_final_label -0.47144851746 0.63586430273091588
## 733          race_final_label -0.42074996522 0.15305394925530741
## 734          race_final_label -0.16756226591 0.66018676901762929
## 735                 ethnicity -0.23273436513 0.33347886020210904
## 736                 ethnicity  0.28233582862 0.35448966539951243
## 737                     rural -0.01757076775 0.92489389560955348
## 738                     rural -0.10951764199 0.50284536393231494
## 739                   smoking -0.17985647561 0.32177091020604093
## 740                   smoking -0.07253382596 0.53558707695853414
## 741          sq_drink_alcohol  0.32796977117 0.03594188193302610
## 742          sq_drink_alcohol  0.02472820551 0.86408706078347330
## 743          sq_drink_alcohol -0.06718987230 0.64121543008337678
## 744  sq_average_drink_per_day -0.27041239489 0.29799235545930247
## 745  sq_average_drink_per_day  0.22209416367 0.59912099850931455
## 746  sq_average_drink_per_day  0.13900622351 0.26608530903584476
## 747             sq_self_hep_b -0.08054960079 0.47899561405698610
## 748             sq_self_hep_b  0.12386342952 0.62208712770531249
## 749             sq_self_hep_c -0.03332494094 0.77046630225326396
## 750             sq_self_hep_c -0.01353349626 0.95592885498327729
## 751         supp_meds_tylenol  0.07478577777 0.84501740098412648
## 752         supp_meds_tylenol -0.27064094184 0.66676216096642582
## 753        supp_meds_steroids  0.14678690410 0.66450267863284707
## 754        supp_meds_steroids -0.05528643906 0.95829689341887170
## 755             sq_water_well -0.07113127787 0.52721094260777346
## 756             sq_water_well -0.02027140684 0.89496451658350551
## 757   sq_water_tap_unfiltered -0.08114647657 0.56352426626744490
## 758   sq_water_tap_unfiltered -0.11994252123 0.36983338632042684
## 759 sq_water_house_filtration -0.02996935033 0.78307622374691188
## 760 sq_water_house_filtration  0.32265583066 0.07896650237649259
## 761    sq_water_faucet_filter  0.09829713530 0.41138610856268598
## 762    sq_water_faucet_filter  0.42992940577 0.00093424336603878
## 763  sq_water_charcoal_filter -0.12033262226 0.27988058283521206
## 764  sq_water_charcoal_filter -0.28953458010 0.06445662344530483
## 765          sq_water_bottled -0.10346737984 0.46520637575138080
## 766          sq_water_bottled -0.10752866806 0.43170112368953550
## 767             sq_water_none -0.01805025876 0.86604968749351308
## 768             sq_water_none  0.31162948316 0.20541041566927390
## 769       sq_water_other_type -0.06912266856 0.52440830804744287
## 770       sq_water_other_type -0.22972534384 0.26277924482141085
##                                                   Factor         PFAS
## 1                                      age_at_enrollment pf_hx_s_scld
## 2                                                    bmi pf_hx_s_scld
## 3                                            trig_mg_d_l pf_hx_s_scld
## 4                                      age_at_enrollment    pfda_scld
## 5                                                    bmi    pfda_scld
## 6                                            trig_mg_d_l    pfda_scld
## 7                                      age_at_enrollment    pfna_scld
## 8                                                    bmi    pfna_scld
## 9                                            trig_mg_d_l    pfna_scld
## 10                                     age_at_enrollment    pfos_scld
## 11                                                   bmi    pfos_scld
## 12                                           trig_mg_d_l    pfos_scld
## 13                                     age_at_enrollment pf_hp_a_scld
## 14                                                   bmi pf_hp_a_scld
## 15                                           trig_mg_d_l pf_hp_a_scld
## 16                                     age_at_enrollment    pfbs_scld
## 17                                                   bmi    pfbs_scld
## 18                                           trig_mg_d_l    pfbs_scld
## 19                                     age_at_enrollment    pfoa_scld
## 20                                                   bmi    pfoa_scld
## 21                                           trig_mg_d_l    pfoa_scld
## 22                                     age_at_enrollment pf_pe_a_scld
## 23                                                   bmi pf_pe_a_scld
## 24                                           trig_mg_d_l pf_pe_a_scld
## 25                                     age_at_enrollment pf_un_a_scld
## 26                                                   bmi pf_un_a_scld
## 27                                           trig_mg_d_l pf_un_a_scld
## 28                                     age_at_enrollment pf_hp_s_scld
## 29                                                   bmi pf_hp_s_scld
## 30                                           trig_mg_d_l pf_hp_s_scld
## 31                                     age_at_enrollment pf_do_a_scld
## 32                                                   bmi pf_do_a_scld
## 33                                           trig_mg_d_l pf_do_a_scld
## 34                                     age_at_enrollment pf_pe_s_scld
## 35                                                   bmi pf_pe_s_scld
## 36                                           trig_mg_d_l pf_pe_s_scld
## 37                                     age_at_enrollment pf_hx_a_scld
## 38                                                   bmi pf_hx_a_scld
## 39                                           trig_mg_d_l pf_hx_a_scld
## 40                                     age_at_enrollment    pfba_scld
## 41                                                   bmi    pfba_scld
## 42                                           trig_mg_d_l    pfba_scld
## 43                                            sourceDUKE pf_hx_s_scld
## 44                                            sourceNCSU pf_hx_s_scld
## 45                                             sourceUNC pf_hx_s_scld
## 46                                               sexMale pf_hx_s_scld
## 47                                     race_eth_labelNHB pf_hx_s_scld
## 48                                     race_eth_labelNHO pf_hx_s_scld
## 49                                     race_eth_labelNHW pf_hx_s_scld
## 50                    race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 51                       race_final_labelAmerican Indian pf_hx_s_scld
## 52        race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 53                                 race_final_labelAsian pf_hx_s_scld
## 54                race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 55                                 race_final_labelBlack pf_hx_s_scld
## 56                    race_final_labelMore than one race pf_hx_s_scld
## 57                                 race_final_labelOther pf_hx_s_scld
## 58                  race_final_labelUnknown/Not Reported pf_hx_s_scld
## 59                                 ethnicityNot Hispanic pf_hx_s_scld
## 60                         ethnicityUnknown/Not Reported pf_hx_s_scld
## 61                             ruralLiving in rural area pf_hx_s_scld
## 62                             ruralUnknown/Not Reported pf_hx_s_scld
## 63                              smokingSmoke or use vape pf_hx_s_scld
## 64                           smokingUnknown/Not Reported pf_hx_s_scld
## 65          sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 66                  sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 67                  sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 68  sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 69  sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 70          sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 71                     sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 72                                      sq_self_hep_bYes pf_hx_s_scld
## 73                     sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 74                                      sq_self_hep_cYes pf_hx_s_scld
## 75                 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 76                                  supp_meds_tylenolYes pf_hx_s_scld
## 77                supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 78                                 supp_meds_steroidsYes pf_hx_s_scld
## 79                     sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 80                                      sq_water_wellYes pf_hx_s_scld
## 81           sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 82                            sq_water_tap_unfilteredYes pf_hx_s_scld
## 83         sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 84                          sq_water_house_filtrationYes pf_hx_s_scld
## 85            sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 86                             sq_water_faucet_filterYes pf_hx_s_scld
## 87          sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 88                           sq_water_charcoal_filterYes pf_hx_s_scld
## 89                  sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 90                                   sq_water_bottledYes pf_hx_s_scld
## 91                     sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 92                                      sq_water_noneYes pf_hx_s_scld
## 93               sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 94                                sq_water_other_typeYes pf_hx_s_scld
## 95                                            sourceDUKE    pfda_scld
## 96                                            sourceNCSU    pfda_scld
## 97                                             sourceUNC    pfda_scld
## 98                                               sexMale    pfda_scld
## 99                                     race_eth_labelNHB    pfda_scld
## 100                                    race_eth_labelNHO    pfda_scld
## 101                                    race_eth_labelNHW    pfda_scld
## 102                   race_eth_labelUnknown/Not Reported    pfda_scld
## 103                      race_final_labelAmerican Indian    pfda_scld
## 104       race_final_labelAmerican Indian/Alaskan Native    pfda_scld
## 105                                race_final_labelAsian    pfda_scld
## 106               race_final_labelAsian/Pacific Islander    pfda_scld
## 107                                race_final_labelBlack    pfda_scld
## 108                   race_final_labelMore than one race    pfda_scld
## 109                                race_final_labelOther    pfda_scld
## 110                 race_final_labelUnknown/Not Reported    pfda_scld
## 111                                ethnicityNot Hispanic    pfda_scld
## 112                        ethnicityUnknown/Not Reported    pfda_scld
## 113                            ruralLiving in rural area    pfda_scld
## 114                            ruralUnknown/Not Reported    pfda_scld
## 115                             smokingSmoke or use vape    pfda_scld
## 116                          smokingUnknown/Not Reported    pfda_scld
## 117         sq_drink_alcoholNo, former drinker (stopped)    pfda_scld
## 118                 sq_drink_alcoholUnknown/Not Reported    pfda_scld
## 119                 sq_drink_alcoholYes, current drinker    pfda_scld
## 120 sq_average_drink_per_day1-2 alcoholic drinks per day    pfda_scld
## 121 sq_average_drink_per_day3-4 alcoholic drinks per day    pfda_scld
## 122         sq_average_drink_per_dayUnknown/Not Reported    pfda_scld
## 123                    sq_self_hep_bUnknown/Not Reported    pfda_scld
## 124                                     sq_self_hep_bYes    pfda_scld
## 125                    sq_self_hep_cUnknown/Not Reported    pfda_scld
## 126                                     sq_self_hep_cYes    pfda_scld
## 127                supp_meds_tylenolUnknown/Not Reported    pfda_scld
## 128                                 supp_meds_tylenolYes    pfda_scld
## 129               supp_meds_steroidsUnknown/Not Reported    pfda_scld
## 130                                supp_meds_steroidsYes    pfda_scld
## 131                    sq_water_wellUnknown/Not Reported    pfda_scld
## 132                                     sq_water_wellYes    pfda_scld
## 133          sq_water_tap_unfilteredUnknown/Not Reported    pfda_scld
## 134                           sq_water_tap_unfilteredYes    pfda_scld
## 135        sq_water_house_filtrationUnknown/Not Reported    pfda_scld
## 136                         sq_water_house_filtrationYes    pfda_scld
## 137           sq_water_faucet_filterUnknown/Not Reported    pfda_scld
## 138                            sq_water_faucet_filterYes    pfda_scld
## 139         sq_water_charcoal_filterUnknown/Not Reported    pfda_scld
## 140                          sq_water_charcoal_filterYes    pfda_scld
## 141                 sq_water_bottledUnknown/Not Reported    pfda_scld
## 142                                  sq_water_bottledYes    pfda_scld
## 143                    sq_water_noneUnknown/Not Reported    pfda_scld
## 144                                     sq_water_noneYes    pfda_scld
## 145              sq_water_other_typeUnknown/Not Reported    pfda_scld
## 146                               sq_water_other_typeYes    pfda_scld
## 147                                           sourceDUKE    pfna_scld
## 148                                           sourceNCSU    pfna_scld
## 149                                            sourceUNC    pfna_scld
## 150                                              sexMale    pfna_scld
## 151                                    race_eth_labelNHB    pfna_scld
## 152                                    race_eth_labelNHO    pfna_scld
## 153                                    race_eth_labelNHW    pfna_scld
## 154                   race_eth_labelUnknown/Not Reported    pfna_scld
## 155                      race_final_labelAmerican Indian    pfna_scld
## 156       race_final_labelAmerican Indian/Alaskan Native    pfna_scld
## 157                                race_final_labelAsian    pfna_scld
## 158               race_final_labelAsian/Pacific Islander    pfna_scld
## 159                                race_final_labelBlack    pfna_scld
## 160                   race_final_labelMore than one race    pfna_scld
## 161                                race_final_labelOther    pfna_scld
## 162                 race_final_labelUnknown/Not Reported    pfna_scld
## 163                                ethnicityNot Hispanic    pfna_scld
## 164                        ethnicityUnknown/Not Reported    pfna_scld
## 165                            ruralLiving in rural area    pfna_scld
## 166                            ruralUnknown/Not Reported    pfna_scld
## 167                             smokingSmoke or use vape    pfna_scld
## 168                          smokingUnknown/Not Reported    pfna_scld
## 169         sq_drink_alcoholNo, former drinker (stopped)    pfna_scld
## 170                 sq_drink_alcoholUnknown/Not Reported    pfna_scld
## 171                 sq_drink_alcoholYes, current drinker    pfna_scld
## 172 sq_average_drink_per_day1-2 alcoholic drinks per day    pfna_scld
## 173 sq_average_drink_per_day3-4 alcoholic drinks per day    pfna_scld
## 174         sq_average_drink_per_dayUnknown/Not Reported    pfna_scld
## 175                    sq_self_hep_bUnknown/Not Reported    pfna_scld
## 176                                     sq_self_hep_bYes    pfna_scld
## 177                    sq_self_hep_cUnknown/Not Reported    pfna_scld
## 178                                     sq_self_hep_cYes    pfna_scld
## 179                supp_meds_tylenolUnknown/Not Reported    pfna_scld
## 180                                 supp_meds_tylenolYes    pfna_scld
## 181               supp_meds_steroidsUnknown/Not Reported    pfna_scld
## 182                                supp_meds_steroidsYes    pfna_scld
## 183                    sq_water_wellUnknown/Not Reported    pfna_scld
## 184                                     sq_water_wellYes    pfna_scld
## 185          sq_water_tap_unfilteredUnknown/Not Reported    pfna_scld
## 186                           sq_water_tap_unfilteredYes    pfna_scld
## 187        sq_water_house_filtrationUnknown/Not Reported    pfna_scld
## 188                         sq_water_house_filtrationYes    pfna_scld
## 189           sq_water_faucet_filterUnknown/Not Reported    pfna_scld
## 190                            sq_water_faucet_filterYes    pfna_scld
## 191         sq_water_charcoal_filterUnknown/Not Reported    pfna_scld
## 192                          sq_water_charcoal_filterYes    pfna_scld
## 193                 sq_water_bottledUnknown/Not Reported    pfna_scld
## 194                                  sq_water_bottledYes    pfna_scld
## 195                    sq_water_noneUnknown/Not Reported    pfna_scld
## 196                                     sq_water_noneYes    pfna_scld
## 197              sq_water_other_typeUnknown/Not Reported    pfna_scld
## 198                               sq_water_other_typeYes    pfna_scld
## 199                                           sourceDUKE    pfos_scld
## 200                                           sourceNCSU    pfos_scld
## 201                                            sourceUNC    pfos_scld
## 202                                              sexMale    pfos_scld
## 203                                    race_eth_labelNHB    pfos_scld
## 204                                    race_eth_labelNHO    pfos_scld
## 205                                    race_eth_labelNHW    pfos_scld
## 206                   race_eth_labelUnknown/Not Reported    pfos_scld
## 207                      race_final_labelAmerican Indian    pfos_scld
## 208       race_final_labelAmerican Indian/Alaskan Native    pfos_scld
## 209                                race_final_labelAsian    pfos_scld
## 210               race_final_labelAsian/Pacific Islander    pfos_scld
## 211                                race_final_labelBlack    pfos_scld
## 212                   race_final_labelMore than one race    pfos_scld
## 213                                race_final_labelOther    pfos_scld
## 214                 race_final_labelUnknown/Not Reported    pfos_scld
## 215                                ethnicityNot Hispanic    pfos_scld
## 216                        ethnicityUnknown/Not Reported    pfos_scld
## 217                            ruralLiving in rural area    pfos_scld
## 218                            ruralUnknown/Not Reported    pfos_scld
## 219                             smokingSmoke or use vape    pfos_scld
## 220                          smokingUnknown/Not Reported    pfos_scld
## 221         sq_drink_alcoholNo, former drinker (stopped)    pfos_scld
## 222                 sq_drink_alcoholUnknown/Not Reported    pfos_scld
## 223                 sq_drink_alcoholYes, current drinker    pfos_scld
## 224 sq_average_drink_per_day1-2 alcoholic drinks per day    pfos_scld
## 225 sq_average_drink_per_day3-4 alcoholic drinks per day    pfos_scld
## 226         sq_average_drink_per_dayUnknown/Not Reported    pfos_scld
## 227                    sq_self_hep_bUnknown/Not Reported    pfos_scld
## 228                                     sq_self_hep_bYes    pfos_scld
## 229                    sq_self_hep_cUnknown/Not Reported    pfos_scld
## 230                                     sq_self_hep_cYes    pfos_scld
## 231                supp_meds_tylenolUnknown/Not Reported    pfos_scld
## 232                                 supp_meds_tylenolYes    pfos_scld
## 233               supp_meds_steroidsUnknown/Not Reported    pfos_scld
## 234                                supp_meds_steroidsYes    pfos_scld
## 235                    sq_water_wellUnknown/Not Reported    pfos_scld
## 236                                     sq_water_wellYes    pfos_scld
## 237          sq_water_tap_unfilteredUnknown/Not Reported    pfos_scld
## 238                           sq_water_tap_unfilteredYes    pfos_scld
## 239        sq_water_house_filtrationUnknown/Not Reported    pfos_scld
## 240                         sq_water_house_filtrationYes    pfos_scld
## 241           sq_water_faucet_filterUnknown/Not Reported    pfos_scld
## 242                            sq_water_faucet_filterYes    pfos_scld
## 243         sq_water_charcoal_filterUnknown/Not Reported    pfos_scld
## 244                          sq_water_charcoal_filterYes    pfos_scld
## 245                 sq_water_bottledUnknown/Not Reported    pfos_scld
## 246                                  sq_water_bottledYes    pfos_scld
## 247                    sq_water_noneUnknown/Not Reported    pfos_scld
## 248                                     sq_water_noneYes    pfos_scld
## 249              sq_water_other_typeUnknown/Not Reported    pfos_scld
## 250                               sq_water_other_typeYes    pfos_scld
## 251                                           sourceDUKE pf_hp_a_scld
## 252                                           sourceNCSU pf_hp_a_scld
## 253                                            sourceUNC pf_hp_a_scld
## 254                                              sexMale pf_hp_a_scld
## 255                                    race_eth_labelNHB pf_hp_a_scld
## 256                                    race_eth_labelNHO pf_hp_a_scld
## 257                                    race_eth_labelNHW pf_hp_a_scld
## 258                   race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 259                      race_final_labelAmerican Indian pf_hp_a_scld
## 260       race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 261                                race_final_labelAsian pf_hp_a_scld
## 262               race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 263                                race_final_labelBlack pf_hp_a_scld
## 264                   race_final_labelMore than one race pf_hp_a_scld
## 265                                race_final_labelOther pf_hp_a_scld
## 266                 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 267                                ethnicityNot Hispanic pf_hp_a_scld
## 268                        ethnicityUnknown/Not Reported pf_hp_a_scld
## 269                            ruralLiving in rural area pf_hp_a_scld
## 270                            ruralUnknown/Not Reported pf_hp_a_scld
## 271                             smokingSmoke or use vape pf_hp_a_scld
## 272                          smokingUnknown/Not Reported pf_hp_a_scld
## 273         sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 274                 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 275                 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 276 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 277 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 278         sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 279                    sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 280                                     sq_self_hep_bYes pf_hp_a_scld
## 281                    sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 282                                     sq_self_hep_cYes pf_hp_a_scld
## 283                supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 284                                 supp_meds_tylenolYes pf_hp_a_scld
## 285               supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 286                                supp_meds_steroidsYes pf_hp_a_scld
## 287                    sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 288                                     sq_water_wellYes pf_hp_a_scld
## 289          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 290                           sq_water_tap_unfilteredYes pf_hp_a_scld
## 291        sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 292                         sq_water_house_filtrationYes pf_hp_a_scld
## 293           sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 294                            sq_water_faucet_filterYes pf_hp_a_scld
## 295         sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 296                          sq_water_charcoal_filterYes pf_hp_a_scld
## 297                 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 298                                  sq_water_bottledYes pf_hp_a_scld
## 299                    sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 300                                     sq_water_noneYes pf_hp_a_scld
## 301              sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 302                               sq_water_other_typeYes pf_hp_a_scld
## 303                                           sourceDUKE    pfbs_scld
## 304                                           sourceNCSU    pfbs_scld
## 305                                            sourceUNC    pfbs_scld
## 306                                              sexMale    pfbs_scld
## 307                                    race_eth_labelNHB    pfbs_scld
## 308                                    race_eth_labelNHO    pfbs_scld
## 309                                    race_eth_labelNHW    pfbs_scld
## 310                   race_eth_labelUnknown/Not Reported    pfbs_scld
## 311                      race_final_labelAmerican Indian    pfbs_scld
## 312       race_final_labelAmerican Indian/Alaskan Native    pfbs_scld
## 313                                race_final_labelAsian    pfbs_scld
## 314               race_final_labelAsian/Pacific Islander    pfbs_scld
## 315                                race_final_labelBlack    pfbs_scld
## 316                   race_final_labelMore than one race    pfbs_scld
## 317                                race_final_labelOther    pfbs_scld
## 318                 race_final_labelUnknown/Not Reported    pfbs_scld
## 319                                ethnicityNot Hispanic    pfbs_scld
## 320                        ethnicityUnknown/Not Reported    pfbs_scld
## 321                            ruralLiving in rural area    pfbs_scld
## 322                            ruralUnknown/Not Reported    pfbs_scld
## 323                             smokingSmoke or use vape    pfbs_scld
## 324                          smokingUnknown/Not Reported    pfbs_scld
## 325         sq_drink_alcoholNo, former drinker (stopped)    pfbs_scld
## 326                 sq_drink_alcoholUnknown/Not Reported    pfbs_scld
## 327                 sq_drink_alcoholYes, current drinker    pfbs_scld
## 328 sq_average_drink_per_day1-2 alcoholic drinks per day    pfbs_scld
## 329 sq_average_drink_per_day3-4 alcoholic drinks per day    pfbs_scld
## 330         sq_average_drink_per_dayUnknown/Not Reported    pfbs_scld
## 331                    sq_self_hep_bUnknown/Not Reported    pfbs_scld
## 332                                     sq_self_hep_bYes    pfbs_scld
## 333                    sq_self_hep_cUnknown/Not Reported    pfbs_scld
## 334                                     sq_self_hep_cYes    pfbs_scld
## 335                supp_meds_tylenolUnknown/Not Reported    pfbs_scld
## 336                                 supp_meds_tylenolYes    pfbs_scld
## 337               supp_meds_steroidsUnknown/Not Reported    pfbs_scld
## 338                                supp_meds_steroidsYes    pfbs_scld
## 339                    sq_water_wellUnknown/Not Reported    pfbs_scld
## 340                                     sq_water_wellYes    pfbs_scld
## 341          sq_water_tap_unfilteredUnknown/Not Reported    pfbs_scld
## 342                           sq_water_tap_unfilteredYes    pfbs_scld
## 343        sq_water_house_filtrationUnknown/Not Reported    pfbs_scld
## 344                         sq_water_house_filtrationYes    pfbs_scld
## 345           sq_water_faucet_filterUnknown/Not Reported    pfbs_scld
## 346                            sq_water_faucet_filterYes    pfbs_scld
## 347         sq_water_charcoal_filterUnknown/Not Reported    pfbs_scld
## 348                          sq_water_charcoal_filterYes    pfbs_scld
## 349                 sq_water_bottledUnknown/Not Reported    pfbs_scld
## 350                                  sq_water_bottledYes    pfbs_scld
## 351                    sq_water_noneUnknown/Not Reported    pfbs_scld
## 352                                     sq_water_noneYes    pfbs_scld
## 353              sq_water_other_typeUnknown/Not Reported    pfbs_scld
## 354                               sq_water_other_typeYes    pfbs_scld
## 355                                           sourceDUKE    pfoa_scld
## 356                                           sourceNCSU    pfoa_scld
## 357                                            sourceUNC    pfoa_scld
## 358                                              sexMale    pfoa_scld
## 359                                    race_eth_labelNHB    pfoa_scld
## 360                                    race_eth_labelNHO    pfoa_scld
## 361                                    race_eth_labelNHW    pfoa_scld
## 362                   race_eth_labelUnknown/Not Reported    pfoa_scld
## 363                      race_final_labelAmerican Indian    pfoa_scld
## 364       race_final_labelAmerican Indian/Alaskan Native    pfoa_scld
## 365                                race_final_labelAsian    pfoa_scld
## 366               race_final_labelAsian/Pacific Islander    pfoa_scld
## 367                                race_final_labelBlack    pfoa_scld
## 368                   race_final_labelMore than one race    pfoa_scld
## 369                                race_final_labelOther    pfoa_scld
## 370                 race_final_labelUnknown/Not Reported    pfoa_scld
## 371                                ethnicityNot Hispanic    pfoa_scld
## 372                        ethnicityUnknown/Not Reported    pfoa_scld
## 373                            ruralLiving in rural area    pfoa_scld
## 374                            ruralUnknown/Not Reported    pfoa_scld
## 375                             smokingSmoke or use vape    pfoa_scld
## 376                          smokingUnknown/Not Reported    pfoa_scld
## 377         sq_drink_alcoholNo, former drinker (stopped)    pfoa_scld
## 378                 sq_drink_alcoholUnknown/Not Reported    pfoa_scld
## 379                 sq_drink_alcoholYes, current drinker    pfoa_scld
## 380 sq_average_drink_per_day1-2 alcoholic drinks per day    pfoa_scld
## 381 sq_average_drink_per_day3-4 alcoholic drinks per day    pfoa_scld
## 382         sq_average_drink_per_dayUnknown/Not Reported    pfoa_scld
## 383                    sq_self_hep_bUnknown/Not Reported    pfoa_scld
## 384                                     sq_self_hep_bYes    pfoa_scld
## 385                    sq_self_hep_cUnknown/Not Reported    pfoa_scld
## 386                                     sq_self_hep_cYes    pfoa_scld
## 387                supp_meds_tylenolUnknown/Not Reported    pfoa_scld
## 388                                 supp_meds_tylenolYes    pfoa_scld
## 389               supp_meds_steroidsUnknown/Not Reported    pfoa_scld
## 390                                supp_meds_steroidsYes    pfoa_scld
## 391                    sq_water_wellUnknown/Not Reported    pfoa_scld
## 392                                     sq_water_wellYes    pfoa_scld
## 393          sq_water_tap_unfilteredUnknown/Not Reported    pfoa_scld
## 394                           sq_water_tap_unfilteredYes    pfoa_scld
## 395        sq_water_house_filtrationUnknown/Not Reported    pfoa_scld
## 396                         sq_water_house_filtrationYes    pfoa_scld
## 397           sq_water_faucet_filterUnknown/Not Reported    pfoa_scld
## 398                            sq_water_faucet_filterYes    pfoa_scld
## 399         sq_water_charcoal_filterUnknown/Not Reported    pfoa_scld
## 400                          sq_water_charcoal_filterYes    pfoa_scld
## 401                 sq_water_bottledUnknown/Not Reported    pfoa_scld
## 402                                  sq_water_bottledYes    pfoa_scld
## 403                    sq_water_noneUnknown/Not Reported    pfoa_scld
## 404                                     sq_water_noneYes    pfoa_scld
## 405              sq_water_other_typeUnknown/Not Reported    pfoa_scld
## 406                               sq_water_other_typeYes    pfoa_scld
## 407                                           sourceDUKE pf_pe_a_scld
## 408                                           sourceNCSU pf_pe_a_scld
## 409                                            sourceUNC pf_pe_a_scld
## 410                                              sexMale pf_pe_a_scld
## 411                                    race_eth_labelNHB pf_pe_a_scld
## 412                                    race_eth_labelNHO pf_pe_a_scld
## 413                                    race_eth_labelNHW pf_pe_a_scld
## 414                   race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 415                      race_final_labelAmerican Indian pf_pe_a_scld
## 416       race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 417                                race_final_labelAsian pf_pe_a_scld
## 418               race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 419                                race_final_labelBlack pf_pe_a_scld
## 420                   race_final_labelMore than one race pf_pe_a_scld
## 421                                race_final_labelOther pf_pe_a_scld
## 422                 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 423                                ethnicityNot Hispanic pf_pe_a_scld
## 424                        ethnicityUnknown/Not Reported pf_pe_a_scld
## 425                            ruralLiving in rural area pf_pe_a_scld
## 426                            ruralUnknown/Not Reported pf_pe_a_scld
## 427                             smokingSmoke or use vape pf_pe_a_scld
## 428                          smokingUnknown/Not Reported pf_pe_a_scld
## 429         sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 430                 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 431                 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 432 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 433 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 434         sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 435                    sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 436                                     sq_self_hep_bYes pf_pe_a_scld
## 437                    sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 438                                     sq_self_hep_cYes pf_pe_a_scld
## 439                supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 440                                 supp_meds_tylenolYes pf_pe_a_scld
## 441               supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 442                                supp_meds_steroidsYes pf_pe_a_scld
## 443                    sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 444                                     sq_water_wellYes pf_pe_a_scld
## 445          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 446                           sq_water_tap_unfilteredYes pf_pe_a_scld
## 447        sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 448                         sq_water_house_filtrationYes pf_pe_a_scld
## 449           sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 450                            sq_water_faucet_filterYes pf_pe_a_scld
## 451         sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 452                          sq_water_charcoal_filterYes pf_pe_a_scld
## 453                 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 454                                  sq_water_bottledYes pf_pe_a_scld
## 455                    sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 456                                     sq_water_noneYes pf_pe_a_scld
## 457              sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 458                               sq_water_other_typeYes pf_pe_a_scld
## 459                                           sourceDUKE pf_un_a_scld
## 460                                           sourceNCSU pf_un_a_scld
## 461                                            sourceUNC pf_un_a_scld
## 462                                              sexMale pf_un_a_scld
## 463                                    race_eth_labelNHB pf_un_a_scld
## 464                                    race_eth_labelNHO pf_un_a_scld
## 465                                    race_eth_labelNHW pf_un_a_scld
## 466                   race_eth_labelUnknown/Not Reported pf_un_a_scld
## 467                      race_final_labelAmerican Indian pf_un_a_scld
## 468       race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 469                                race_final_labelAsian pf_un_a_scld
## 470               race_final_labelAsian/Pacific Islander pf_un_a_scld
## 471                                race_final_labelBlack pf_un_a_scld
## 472                   race_final_labelMore than one race pf_un_a_scld
## 473                                race_final_labelOther pf_un_a_scld
## 474                 race_final_labelUnknown/Not Reported pf_un_a_scld
## 475                                ethnicityNot Hispanic pf_un_a_scld
## 476                        ethnicityUnknown/Not Reported pf_un_a_scld
## 477                            ruralLiving in rural area pf_un_a_scld
## 478                            ruralUnknown/Not Reported pf_un_a_scld
## 479                             smokingSmoke or use vape pf_un_a_scld
## 480                          smokingUnknown/Not Reported pf_un_a_scld
## 481         sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 482                 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 483                 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 484 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 485 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 486         sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 487                    sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 488                                     sq_self_hep_bYes pf_un_a_scld
## 489                    sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 490                                     sq_self_hep_cYes pf_un_a_scld
## 491                supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 492                                 supp_meds_tylenolYes pf_un_a_scld
## 493               supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 494                                supp_meds_steroidsYes pf_un_a_scld
## 495                    sq_water_wellUnknown/Not Reported pf_un_a_scld
## 496                                     sq_water_wellYes pf_un_a_scld
## 497          sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 498                           sq_water_tap_unfilteredYes pf_un_a_scld
## 499        sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 500                         sq_water_house_filtrationYes pf_un_a_scld
## 501           sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 502                            sq_water_faucet_filterYes pf_un_a_scld
## 503         sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 504                          sq_water_charcoal_filterYes pf_un_a_scld
## 505                 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 506                                  sq_water_bottledYes pf_un_a_scld
## 507                    sq_water_noneUnknown/Not Reported pf_un_a_scld
## 508                                     sq_water_noneYes pf_un_a_scld
## 509              sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 510                               sq_water_other_typeYes pf_un_a_scld
## 511                                           sourceDUKE pf_hp_s_scld
## 512                                           sourceNCSU pf_hp_s_scld
## 513                                            sourceUNC pf_hp_s_scld
## 514                                              sexMale pf_hp_s_scld
## 515                                    race_eth_labelNHB pf_hp_s_scld
## 516                                    race_eth_labelNHO pf_hp_s_scld
## 517                                    race_eth_labelNHW pf_hp_s_scld
## 518                   race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 519                      race_final_labelAmerican Indian pf_hp_s_scld
## 520       race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 521                                race_final_labelAsian pf_hp_s_scld
## 522               race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 523                                race_final_labelBlack pf_hp_s_scld
## 524                   race_final_labelMore than one race pf_hp_s_scld
## 525                                race_final_labelOther pf_hp_s_scld
## 526                 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 527                                ethnicityNot Hispanic pf_hp_s_scld
## 528                        ethnicityUnknown/Not Reported pf_hp_s_scld
## 529                            ruralLiving in rural area pf_hp_s_scld
## 530                            ruralUnknown/Not Reported pf_hp_s_scld
## 531                             smokingSmoke or use vape pf_hp_s_scld
## 532                          smokingUnknown/Not Reported pf_hp_s_scld
## 533         sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 534                 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 535                 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 536 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 537 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 538         sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 539                    sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 540                                     sq_self_hep_bYes pf_hp_s_scld
## 541                    sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 542                                     sq_self_hep_cYes pf_hp_s_scld
## 543                supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 544                                 supp_meds_tylenolYes pf_hp_s_scld
## 545               supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 546                                supp_meds_steroidsYes pf_hp_s_scld
## 547                    sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 548                                     sq_water_wellYes pf_hp_s_scld
## 549          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 550                           sq_water_tap_unfilteredYes pf_hp_s_scld
## 551        sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 552                         sq_water_house_filtrationYes pf_hp_s_scld
## 553           sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 554                            sq_water_faucet_filterYes pf_hp_s_scld
## 555         sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 556                          sq_water_charcoal_filterYes pf_hp_s_scld
## 557                 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 558                                  sq_water_bottledYes pf_hp_s_scld
## 559                    sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 560                                     sq_water_noneYes pf_hp_s_scld
## 561              sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 562                               sq_water_other_typeYes pf_hp_s_scld
## 563                                           sourceDUKE pf_do_a_scld
## 564                                           sourceNCSU pf_do_a_scld
## 565                                            sourceUNC pf_do_a_scld
## 566                                              sexMale pf_do_a_scld
## 567                                    race_eth_labelNHB pf_do_a_scld
## 568                                    race_eth_labelNHO pf_do_a_scld
## 569                                    race_eth_labelNHW pf_do_a_scld
## 570                   race_eth_labelUnknown/Not Reported pf_do_a_scld
## 571                      race_final_labelAmerican Indian pf_do_a_scld
## 572       race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 573                                race_final_labelAsian pf_do_a_scld
## 574               race_final_labelAsian/Pacific Islander pf_do_a_scld
## 575                                race_final_labelBlack pf_do_a_scld
## 576                   race_final_labelMore than one race pf_do_a_scld
## 577                                race_final_labelOther pf_do_a_scld
## 578                 race_final_labelUnknown/Not Reported pf_do_a_scld
## 579                                ethnicityNot Hispanic pf_do_a_scld
## 580                        ethnicityUnknown/Not Reported pf_do_a_scld
## 581                            ruralLiving in rural area pf_do_a_scld
## 582                            ruralUnknown/Not Reported pf_do_a_scld
## 583                             smokingSmoke or use vape pf_do_a_scld
## 584                          smokingUnknown/Not Reported pf_do_a_scld
## 585         sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 586                 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 587                 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 588 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 589 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 590         sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 591                    sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 592                                     sq_self_hep_bYes pf_do_a_scld
## 593                    sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 594                                     sq_self_hep_cYes pf_do_a_scld
## 595                supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 596                                 supp_meds_tylenolYes pf_do_a_scld
## 597               supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 598                                supp_meds_steroidsYes pf_do_a_scld
## 599                    sq_water_wellUnknown/Not Reported pf_do_a_scld
## 600                                     sq_water_wellYes pf_do_a_scld
## 601          sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 602                           sq_water_tap_unfilteredYes pf_do_a_scld
## 603        sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 604                         sq_water_house_filtrationYes pf_do_a_scld
## 605           sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 606                            sq_water_faucet_filterYes pf_do_a_scld
## 607         sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 608                          sq_water_charcoal_filterYes pf_do_a_scld
## 609                 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 610                                  sq_water_bottledYes pf_do_a_scld
## 611                    sq_water_noneUnknown/Not Reported pf_do_a_scld
## 612                                     sq_water_noneYes pf_do_a_scld
## 613              sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 614                               sq_water_other_typeYes pf_do_a_scld
## 615                                           sourceDUKE pf_pe_s_scld
## 616                                           sourceNCSU pf_pe_s_scld
## 617                                            sourceUNC pf_pe_s_scld
## 618                                              sexMale pf_pe_s_scld
## 619                                    race_eth_labelNHB pf_pe_s_scld
## 620                                    race_eth_labelNHO pf_pe_s_scld
## 621                                    race_eth_labelNHW pf_pe_s_scld
## 622                   race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 623                      race_final_labelAmerican Indian pf_pe_s_scld
## 624       race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 625                                race_final_labelAsian pf_pe_s_scld
## 626               race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 627                                race_final_labelBlack pf_pe_s_scld
## 628                   race_final_labelMore than one race pf_pe_s_scld
## 629                                race_final_labelOther pf_pe_s_scld
## 630                 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 631                                ethnicityNot Hispanic pf_pe_s_scld
## 632                        ethnicityUnknown/Not Reported pf_pe_s_scld
## 633                            ruralLiving in rural area pf_pe_s_scld
## 634                            ruralUnknown/Not Reported pf_pe_s_scld
## 635                             smokingSmoke or use vape pf_pe_s_scld
## 636                          smokingUnknown/Not Reported pf_pe_s_scld
## 637         sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 638                 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 639                 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 640 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 641 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 642         sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 643                    sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 644                                     sq_self_hep_bYes pf_pe_s_scld
## 645                    sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 646                                     sq_self_hep_cYes pf_pe_s_scld
## 647                supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 648                                 supp_meds_tylenolYes pf_pe_s_scld
## 649               supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 650                                supp_meds_steroidsYes pf_pe_s_scld
## 651                    sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 652                                     sq_water_wellYes pf_pe_s_scld
## 653          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 654                           sq_water_tap_unfilteredYes pf_pe_s_scld
## 655        sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 656                         sq_water_house_filtrationYes pf_pe_s_scld
## 657           sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 658                            sq_water_faucet_filterYes pf_pe_s_scld
## 659         sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 660                          sq_water_charcoal_filterYes pf_pe_s_scld
## 661                 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 662                                  sq_water_bottledYes pf_pe_s_scld
## 663                    sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 664                                     sq_water_noneYes pf_pe_s_scld
## 665              sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 666                               sq_water_other_typeYes pf_pe_s_scld
## 667                                           sourceDUKE pf_hx_a_scld
## 668                                           sourceNCSU pf_hx_a_scld
## 669                                            sourceUNC pf_hx_a_scld
## 670                                              sexMale pf_hx_a_scld
## 671                                    race_eth_labelNHB pf_hx_a_scld
## 672                                    race_eth_labelNHO pf_hx_a_scld
## 673                                    race_eth_labelNHW pf_hx_a_scld
## 674                   race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 675                      race_final_labelAmerican Indian pf_hx_a_scld
## 676       race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 677                                race_final_labelAsian pf_hx_a_scld
## 678               race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 679                                race_final_labelBlack pf_hx_a_scld
## 680                   race_final_labelMore than one race pf_hx_a_scld
## 681                                race_final_labelOther pf_hx_a_scld
## 682                 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 683                                ethnicityNot Hispanic pf_hx_a_scld
## 684                        ethnicityUnknown/Not Reported pf_hx_a_scld
## 685                            ruralLiving in rural area pf_hx_a_scld
## 686                            ruralUnknown/Not Reported pf_hx_a_scld
## 687                             smokingSmoke or use vape pf_hx_a_scld
## 688                          smokingUnknown/Not Reported pf_hx_a_scld
## 689         sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 690                 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 691                 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 692 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 693 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 694         sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 695                    sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 696                                     sq_self_hep_bYes pf_hx_a_scld
## 697                    sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 698                                     sq_self_hep_cYes pf_hx_a_scld
## 699                supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 700                                 supp_meds_tylenolYes pf_hx_a_scld
## 701               supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 702                                supp_meds_steroidsYes pf_hx_a_scld
## 703                    sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 704                                     sq_water_wellYes pf_hx_a_scld
## 705          sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 706                           sq_water_tap_unfilteredYes pf_hx_a_scld
## 707        sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 708                         sq_water_house_filtrationYes pf_hx_a_scld
## 709           sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 710                            sq_water_faucet_filterYes pf_hx_a_scld
## 711         sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 712                          sq_water_charcoal_filterYes pf_hx_a_scld
## 713                 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 714                                  sq_water_bottledYes pf_hx_a_scld
## 715                    sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 716                                     sq_water_noneYes pf_hx_a_scld
## 717              sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 718                               sq_water_other_typeYes pf_hx_a_scld
## 719                                           sourceDUKE    pfba_scld
## 720                                           sourceNCSU    pfba_scld
## 721                                            sourceUNC    pfba_scld
## 722                                              sexMale    pfba_scld
## 723                                    race_eth_labelNHB    pfba_scld
## 724                                    race_eth_labelNHO    pfba_scld
## 725                                    race_eth_labelNHW    pfba_scld
## 726                   race_eth_labelUnknown/Not Reported    pfba_scld
## 727                      race_final_labelAmerican Indian    pfba_scld
## 728       race_final_labelAmerican Indian/Alaskan Native    pfba_scld
## 729                                race_final_labelAsian    pfba_scld
## 730               race_final_labelAsian/Pacific Islander    pfba_scld
## 731                                race_final_labelBlack    pfba_scld
## 732                   race_final_labelMore than one race    pfba_scld
## 733                                race_final_labelOther    pfba_scld
## 734                 race_final_labelUnknown/Not Reported    pfba_scld
## 735                                ethnicityNot Hispanic    pfba_scld
## 736                        ethnicityUnknown/Not Reported    pfba_scld
## 737                            ruralLiving in rural area    pfba_scld
## 738                            ruralUnknown/Not Reported    pfba_scld
## 739                             smokingSmoke or use vape    pfba_scld
## 740                          smokingUnknown/Not Reported    pfba_scld
## 741         sq_drink_alcoholNo, former drinker (stopped)    pfba_scld
## 742                 sq_drink_alcoholUnknown/Not Reported    pfba_scld
## 743                 sq_drink_alcoholYes, current drinker    pfba_scld
## 744 sq_average_drink_per_day1-2 alcoholic drinks per day    pfba_scld
## 745 sq_average_drink_per_day3-4 alcoholic drinks per day    pfba_scld
## 746         sq_average_drink_per_dayUnknown/Not Reported    pfba_scld
## 747                    sq_self_hep_bUnknown/Not Reported    pfba_scld
## 748                                     sq_self_hep_bYes    pfba_scld
## 749                    sq_self_hep_cUnknown/Not Reported    pfba_scld
## 750                                     sq_self_hep_cYes    pfba_scld
## 751                supp_meds_tylenolUnknown/Not Reported    pfba_scld
## 752                                 supp_meds_tylenolYes    pfba_scld
## 753               supp_meds_steroidsUnknown/Not Reported    pfba_scld
## 754                                supp_meds_steroidsYes    pfba_scld
## 755                    sq_water_wellUnknown/Not Reported    pfba_scld
## 756                                     sq_water_wellYes    pfba_scld
## 757          sq_water_tap_unfilteredUnknown/Not Reported    pfba_scld
## 758                           sq_water_tap_unfilteredYes    pfba_scld
## 759        sq_water_house_filtrationUnknown/Not Reported    pfba_scld
## 760                         sq_water_house_filtrationYes    pfba_scld
## 761           sq_water_faucet_filterUnknown/Not Reported    pfba_scld
## 762                            sq_water_faucet_filterYes    pfba_scld
## 763         sq_water_charcoal_filterUnknown/Not Reported    pfba_scld
## 764                          sq_water_charcoal_filterYes    pfba_scld
## 765                 sq_water_bottledUnknown/Not Reported    pfba_scld
## 766                                  sq_water_bottledYes    pfba_scld
## 767                    sq_water_noneUnknown/Not Reported    pfba_scld
## 768                                     sq_water_noneYes    pfba_scld
## 769              sq_water_other_typeUnknown/Not Reported    pfba_scld
## 770                               sq_water_other_typeYes    pfba_scld
##
ggplot(all_results, aes(x = "", y = Coeff)) +
  geom_boxplot(fill = "lightblue", color = "blue") +
  labs(title = "Box Plot of Coefficients", y = "Coefficient") +
  theme_minimal()   

##


all_results <- all_results %>%
  mutate(


fancy_table <- set_caption(fancy_table, caption = "Confusion matrix results table (N=349)")

fancy_table <- fontsize(fancy_table, size = 8, part = "all") # Reduce font size of values
fancy_table <- fontsize(fancy_table, size = 9, part = "header") # Reduce font size of header

print(fancy_table)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 30 row(s) 
## original dataset sample: 
##                                                                  Comparison
## lowest cut ast vs highest cut ast         lowest cut ast vs highest cut ast
## lowest cut ast vs lowest cut alt           lowest cut ast vs lowest cut alt
## lowest cut ast vs highest cut alt         lowest cut ast vs highest cut alt
## lowest cut ast vs cirrhosis                     lowest cut ast vs cirrhosis
## lowest cut ast vs cirrhosis diagnosed lowest cut ast vs cirrhosis diagnosed
##                                       Sensitivity Specificity   PPV   NPV
## lowest cut ast vs highest cut ast           0.743           1     1 0.690
## lowest cut ast vs lowest cut alt            0.221           1     1 0.423
## lowest cut ast vs highest cut alt           0.126           1     1 0.396
## lowest cut ast vs cirrhosis                 0.968       0.008 0.630 0.125
## lowest cut ast vs cirrhosis diagnosed       0.498       0.752 0.783 0.455
##                                       Accuracy F1_Score Kappa
## lowest cut ast vs highest cut ast        0.837    0.853     0
## lowest cut ast vs lowest cut alt         0.504    0.362     0
## lowest cut ast vs highest cut alt        0.444    0.224     0
## lowest cut ast vs cirrhosis              0.619    0.764     0
## lowest cut ast vs cirrhosis diagnosed    0.589    0.608     0
#2. setting case_control as gold standard in total ppl
results <- list()
seen_comparisons <- character()  # Initialize seen_comparisons as a character vector

data_renamed <- data[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))

variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'

for (i in seq_along(variables)) {
  for (j in seq_along(variables)) {
    if (i != j) {
      # Define the comparison names
      comparison_name_1 <- paste(variables[i], "vs", variables[j])
      comparison_name_2 <- paste(variables[j], "vs", variables[i])
      
      # Skip if the reverse comparison has already been processed
      if (comparison_name_2 %in% seen_comparisons) {
        next
      }
      
      # Mark the first comparison as seen
      seen_comparisons <- c(seen_comparisons, comparison_name_1)
      
      # Ensure both variables have the same length and no NAs
      if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
        
        # Remove rows with NA values in either variable
        valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
        valid_data_i <- data_renamed[[variables[i]]][valid_indices]
        valid_data_j <- data_renamed[[variables[j]]][valid_indices]
        
        # Check if gold standard is involved
        if (variables[j] == gold_standard) {
          predicted <- valid_data_i
          actual <- valid_data_j
        } else if (variables[i] == gold_standard) {
          predicted <- valid_data_j
          actual <- valid_data_i
        } else {
          next
        }
        
        # Generate confusion matrix
        conf_matrix <- table(predicted = predicted, actual = actual)
        
        # Print confusion matrix for debugging
        print(paste("Confusion Matrix for", comparison_name_1))
        print(conf_matrix)
        
        # Check for at least one Abnormal and one Normal class in both predicted and actual
        levels_i <- rownames(conf_matrix)
        levels_j <- colnames(conf_matrix)
        
        if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
            "Normal" %in% levels_i && "Normal" %in% levels_j) {
          
          # Extract values safely
          true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
          false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
          
          # Calculate metrics
          sensitivity <- true_pos / (true_pos + false_neg)
          specificity <- true_neg / (true_neg + false_pos)
          ppv <- true_pos / (true_pos + false_pos)
          npv <- true_neg / (true_neg + false_neg)
          accuracy <- (true_pos + true_neg) / sum(conf_matrix)
          
          # Ensure predicted is numeric and actual is a factor
          predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
          actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
          
          # ROC Curve and AUC
          roc_curve <- tryCatch({
            roc(actual_factor, predicted_numeric)
          }, error = function(e) {
            print("Error in ROC calculation")
            return(NULL)
          })
          
          auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
          
          # F1 Score
          f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
            2 * (ppv * sensitivity) / (ppv + sensitivity)
          } else {
            NA
          }
          
          # Kappa Statistic
          kappa_value <- tryCatch({
            kappa2(conf_matrix)$value
          }, error = function(e) {
            print("Error in Kappa calculation")
            return(NA)
          })
          
          # Store results
          results[[comparison_name_1]] <- c(
            Sensitivity = sensitivity,
            Specificity = specificity,
            PPV = ppv,
            NPV = npv,
            Accuracy = accuracy,
            AUC = auc_value,
            F1_Score = f1_score,
            Kappa = kappa_value
          )
        } else {
          results[[comparison_name_1]] <- c(
            Sensitivity = NA,
            Specificity = NA,
            PPV = NA,
            NPV = NA,
            Accuracy = NA,
            AUC = NA,
            F1_Score = NA,
            Kappa = NA
          )
        }
      } else {
        results[[comparison_name_1]] <- c(
          Sensitivity = NA,
          Specificity = NA,
          PPV = NA,
          NPV = NA,
          Accuracy = NA,
          AUC = NA,
          F1_Score = NA,
          Kappa = NA
        )
      }
    }
  }
}
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      101    102
##   Normal         28     85
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       90     67
##   Normal         39    120
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       25     22
##   Normal        104    165
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal       14     14
##   Normal        115    173
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal      128    180
##   Normal          1      7
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)

# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)

# Format results
results_df <- results_df %>%
  mutate(across(everything(), ~ case_when(
    . == 1.000 ~ "1",
    . == 0.000 ~ "0",
    TRUE ~ sprintf("%.3f", .)
  )))

results_df$Comparison <- rownames(results_df)

# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())

# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
  filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))

# Create flextable
fancy_table1 <- flextable(results_df)
fancy_table1 <- set_table_properties(fancy_table1, width = 0.8, layout = "autofit")

fancy_table1 <- set_caption(fancy_table1, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) in total ppl (N=349)")

fancy_table1 <- fontsize(fancy_table1, size = 8, part = "all") # Reduce font size of values
fancy_table1 <- fontsize(fancy_table1, size = 9, part = "header") # Reduce font size of header

# Print flextable
print(fancy_table1)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 5 row(s) 
## original dataset sample: 
##                                                                    Comparison
## lowest cut ast vs cirrhosis diagnosed   lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed   lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed             cirrhosis vs cirrhosis diagnosed
##                                        Sensitivity Specificity   PPV   NPV
## lowest cut ast vs cirrhosis diagnosed        0.498       0.752 0.783 0.455
## highest cut ast vs cirrhosis diagnosed       0.573       0.755 0.698 0.642
## lowest cut alt vs cirrhosis diagnosed        0.532       0.613 0.194 0.882
## highest cut alt vs cirrhosis diagnosed       0.500       0.601 0.109 0.925
## cirrhosis vs cirrhosis diagnosed             0.416       0.875 0.992 0.037
##                                        Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed     0.589    0.608     0
## highest cut ast vs cirrhosis diagnosed    0.665    0.629     0
## lowest cut alt vs cirrhosis diagnosed     0.601    0.284     0
## highest cut alt vs cirrhosis diagnosed    0.592    0.178 0.333
## cirrhosis vs cirrhosis diagnosed          0.427    0.586     0
############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)

results_long <- results_df %>%
  pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")

results_long <- results_long %>%
  mutate(Value = as.numeric(Value)) %>%
  filter(!is.na(Value)) 

new_labels <- c(
  "cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
  "highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
  "lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
  "highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
  "lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)

palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")  

# Recode the Comparison column
results_long <- results_long %>%
  mutate(Comparison = recode(Comparison, !!!new_labels))


# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
  geom_point(size = 3) +
  labs(title = "Diagnostic testing in total",
       x = "Scores",
       y = "Measure of diagnostic test performance",
       color = "Comparison",
       shape = "Comparison") +
  scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
  theme(axis.text.y = element_text(size = 10),
        axis.text.x = element_text(size = 10),
        axis.title = element_text(size = 12),
        legend.position = "right") +
  scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
  scale_color_manual(values = palette_colors)

# 3. after excluding NCSU data
results <- list()
seen_comparisons <- character()  # Initialize seen_comparisons as a character vector

data_renamed <- data_ex_ncsu[, c('ast_cat1', 'ast_cat2', 'alt_cat1', 'alt_cat2', 'cirrhosis', 'case_control')]
colnames(data_renamed) <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

data_renamed$cirrhosis <- factor(data_renamed$cirrhosis, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))
data_renamed$`cirrhosis diagnosed` <- factor(data_renamed$`cirrhosis diagnosed`, levels = c("Cirrhosis", "Healthy"), labels = c("Abnormal", "Normal"))

variables <- c('lowest cut ast', 'highest cut ast', 'lowest cut alt', 'highest cut alt', 'cirrhosis', 'cirrhosis diagnosed')

# Identify the gold standard variable
gold_standard <- 'cirrhosis diagnosed'

for (i in seq_along(variables)) {
  for (j in seq_along(variables)) {
    if (i != j) {
      # Define the comparison names
      comparison_name_1 <- paste(variables[i], "vs", variables[j])
      comparison_name_2 <- paste(variables[j], "vs", variables[i])
      
      # Skip if the reverse comparison has already been processed
      if (comparison_name_2 %in% seen_comparisons) {
        next
      }
      
      # Mark the first comparison as seen
      seen_comparisons <- c(seen_comparisons, comparison_name_1)
      
      # Ensure both variables have the same length and no NAs
      if (length(data_renamed[[variables[i]]]) == length(data_renamed[[variables[j]]])) {
        
        # Remove rows with NA values in either variable
        valid_indices <- complete.cases(data_renamed[[variables[i]]], data_renamed[[variables[j]]])
        valid_data_i <- data_renamed[[variables[i]]][valid_indices]
        valid_data_j <- data_renamed[[variables[j]]][valid_indices]
        
        # Check if gold standard is involved
        if (variables[j] == gold_standard) {
          predicted <- valid_data_i
          actual <- valid_data_j
        } else if (variables[i] == gold_standard) {
          predicted <- valid_data_j
          actual <- valid_data_i
        } else {
          next
        }
        
        # Generate confusion matrix
        conf_matrix <- table(predicted = predicted, actual = actual)
        
        # Print confusion matrix for debugging
        print(paste("Confusion Matrix for", comparison_name_1))
        print(conf_matrix)
        
        # Check for at least one Abnormal and one Normal class in both predicted and actual
        levels_i <- rownames(conf_matrix)
        levels_j <- colnames(conf_matrix)
        
        if ("Abnormal" %in% levels_i && "Abnormal" %in% levels_j &&
            "Normal" %in% levels_i && "Normal" %in% levels_j) {
          
          # Extract values safely
          true_pos <- conf_matrix["Abnormal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          false_neg <- conf_matrix["Abnormal", "Normal"] %>% ifelse(is.na(.), 0, .)
          false_pos <- conf_matrix["Normal", "Abnormal"] %>% ifelse(is.na(.), 0, .)
          true_neg <- conf_matrix["Normal", "Normal"] %>% ifelse(is.na(.), 0, .)
          
          # Calculate metrics
          sensitivity <- true_pos / (true_pos + false_neg)
          specificity <- true_neg / (true_neg + false_pos)
          ppv <- true_pos / (true_pos + false_pos)
          npv <- true_neg / (true_neg + false_neg)
          accuracy <- (true_pos + true_neg) / sum(conf_matrix)
          
          # Ensure predicted is numeric and actual is a factor
          predicted_numeric <- as.numeric(factor(predicted, levels = unique(predicted)))
          actual_factor <- factor(actual, levels = c("Normal", "Abnormal")) # Assuming "Abnormal" is positive class
          
          # ROC Curve and AUC
          roc_curve <- tryCatch({
            roc(actual_factor, predicted_numeric)
          }, error = function(e) {
            print("Error in ROC calculation")
            return(NULL)
          })
          
          auc_value <- if (!is.null(roc_curve)) auc(roc_curve) else NA
          
          # F1 Score
          f1_score <- if (!is.na(ppv + sensitivity) && ppv + sensitivity > 0) {
            2 * (ppv * sensitivity) / (ppv + sensitivity)
          } else {
            NA
          }
          
          # Kappa Statistic
          kappa_value <- tryCatch({
            kappa2(conf_matrix)$value
          }, error = function(e) {
            print("Error in Kappa calculation")
            return(NA)
          })
          
          # Store results
          results[[comparison_name_1]] <- c(
            Sensitivity = sensitivity,
            Specificity = specificity,
            PPV = ppv,
            NPV = npv,
            Accuracy = accuracy,
            AUC = auc_value,
            F1_Score = f1_score,
            Kappa = kappa_value
          )
        } else {
          results[[comparison_name_1]] <- c(
            Sensitivity = NA,
            Specificity = NA,
            PPV = NA,
            NPV = NA,
            Accuracy = NA,
            AUC = NA,
            F1_Score = NA,
            Kappa = NA
          )
        }
      } else {
        results[[comparison_name_1]] <- c(
          Sensitivity = NA,
          Specificity = NA,
          PPV = NA,
          NPV = NA,
          Accuracy = NA,
          AUC = NA,
          F1_Score = NA,
          Kappa = NA
        )
      }
    }
  }
}
## [1] "Confusion Matrix for lowest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        2     67
##   Normal          0     71
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut ast vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        1     34
##   Normal          1    104
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for lowest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        0      8
##   Normal          2    130
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for highest cut alt vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        0      5
##   Normal          2    133
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
## [1] "Confusion Matrix for cirrhosis vs cirrhosis diagnosed"
##           actual
## predicted  Abnormal Normal
##   Abnormal        2    135
##   Normal          0      3
## Setting levels: control = Normal, case = Abnormal
## Setting direction: controls < cases
# Convert results to dataframe
results_df <- do.call(rbind, results)
results_df <- as.data.frame(results_df)

# Remove AUC column
results_df <- results_df %>% dplyr::select(-AUC)

# Format results
results_df <- results_df %>%
  mutate(across(everything(), ~ case_when(
    . == 1.000 ~ "1",
    . == 0.000 ~ "0",
    TRUE ~ sprintf("%.3f", .)
  )))

results_df$Comparison <- rownames(results_df)

# Reorder columns
results_df <- results_df %>% dplyr::select(Comparison, everything())

# Filter to keep only the first comparison result (ignoring reversed comparisons)
results_df <- results_df %>%
  filter(!grepl("vs", Comparison) | !duplicated(gsub("vs.*", "", Comparison)))

# Create flextable
fancy_table2 <- flextable(results_df)
fancy_table2 <- set_table_properties(fancy_table2, width = 0.8, layout = "autofit")

fancy_table2 <- set_caption(fancy_table2, caption = "Diagnosis of new categorical endpoints compared to case control status (gold standard) after excluding NCSU (N=173)")

fancy_table2 <- fontsize(fancy_table2, size = 8, part = "all") # Reduce font size of values
fancy_table2 <- fontsize(fancy_table2, size = 9, part = "header") # Reduce font size of header

# Print flextable
print(fancy_table2)
## a flextable object.
## col_keys: `Comparison`, `Sensitivity`, `Specificity`, `PPV`, `NPV`, `Accuracy`, `F1_Score`, `Kappa` 
## header has 1 row(s) 
## body has 5 row(s) 
## original dataset sample: 
##                                                                    Comparison
## lowest cut ast vs cirrhosis diagnosed   lowest cut ast vs cirrhosis diagnosed
## highest cut ast vs cirrhosis diagnosed highest cut ast vs cirrhosis diagnosed
## lowest cut alt vs cirrhosis diagnosed   lowest cut alt vs cirrhosis diagnosed
## highest cut alt vs cirrhosis diagnosed highest cut alt vs cirrhosis diagnosed
## cirrhosis vs cirrhosis diagnosed             cirrhosis vs cirrhosis diagnosed
##                                        Sensitivity Specificity   PPV   NPV
## lowest cut ast vs cirrhosis diagnosed        0.029           1     1 0.514
## highest cut ast vs cirrhosis diagnosed       0.029       0.990 0.500 0.754
## lowest cut alt vs cirrhosis diagnosed            0       0.985     0 0.942
## highest cut alt vs cirrhosis diagnosed           0       0.985     0 0.964
## cirrhosis vs cirrhosis diagnosed             0.015           1     1 0.022
##                                        Accuracy F1_Score Kappa
## lowest cut ast vs cirrhosis diagnosed     0.521    0.056     0
## highest cut ast vs cirrhosis diagnosed    0.750    0.054     0
## lowest cut alt vs cirrhosis diagnosed     0.929       NA     0
## highest cut alt vs cirrhosis diagnosed    0.950       NA     0
## cirrhosis vs cirrhosis diagnosed          0.036    0.029     0
###
doc <- read_docx()
doc <- body_add_flextable(doc, fancy_table)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")

doc <- body_add_flextable(doc, fancy_table1)
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_par(doc, value = " ", style = "Normal")
doc <- body_add_flextable(doc, fancy_table2)

print(doc, target = "Categorical endpoint diagnosis.docx")


############### scatter plot
results_df <- results_df %>% dplyr::select(-Kappa)

results_long <- results_df %>%
  pivot_longer(cols = -Comparison, names_to = "Metric", values_to = "Value")

results_long <- results_long %>%
  mutate(Value = as.numeric(Value)) %>%
  filter(!is.na(Value)) 
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Value = as.numeric(Value)`.
## Caused by warning:
## ! NAs introduced by coercion
new_labels <- c(
  "cirrhosis vs cirrhosis diagnosed" = "cirrhosis by ast/alt>1",
  "highest cut alt vs cirrhosis diagnosed" = "highest cut alt",
  "lowest cut alt vs cirrhosis diagnosed" = "lowest cut alt",
  "highest cut ast vs cirrhosis diagnosed" = "highest cut ast",
  "lowest cut ast vs cirrhosis diagnosed" = "lowest cut ast"
)

palette_colors <- brewer.pal(n = length(unique(results_long$Comparison)), name = "Set1")  

# Recode the Comparison column
results_long <- results_long %>%
  mutate(Comparison = recode(Comparison, !!!new_labels))



# Create the scatter plot
ggplot(results_long, aes(x = Value, y = Metric, color = Comparison, shape = Comparison)) +
  geom_point(size = 3) +
  labs(title = "Diagnostic testing after excluding NCSU",
       x = "Scores",
       y = "Measure of diagnostic test performance",
       color = "Comparison",
       shape = "Comparison") +
  scale_x_continuous(limits = c(0, 1), breaks = seq(0, 1, 0.1)) + # Adjust x-axis
  theme(axis.text.y = element_text(size = 10),
        axis.text.x = element_text(size = 10),
        axis.title = element_text(size = 12),
        legend.position = "right") +
  scale_shape_manual(values = 1:length(unique(results_long$Comparison))) +
  scale_color_manual(values = palette_colors)

Correlation heat map btw PFAS and potential confounders

# reset up reference category of some potential confounders
vars_to_convert <- c("source", "race_final_label","sq_drink_alcohol","sq_average_drink_per_day")
data[vars_to_convert] <- lapply(data[vars_to_convert], as.factor)

data$source <- relevel(data$source, ref = "Emory")
data$race_final_label <- relevel(data$race_final_label, ref = "White")
data$sq_drink_alcohol <- relevel(data$sq_drink_alcohol, ref = "No, never drinker")
data$sq_average_drink_per_day <- relevel(data$sq_average_drink_per_day, ref = "Less than 1 alcoholic drink per day")

# Define continuous potential confounders
numeric_data <- data %>%
     dplyr::select(all_of(potential_conf)) %>%
     dplyr::select_if(is.numeric)

conti_conf <- names(numeric_data)

# Function to extract model summary for continuous covariates
extract_model_summary <- function(pfas, covariate, data) {
  model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
  summary_model <- summary(model)
  estimate <- coef(summary_model)[2, 1]
  p_value <- coef(summary_model)[2, 4]
  return(data.frame(Confounders = covariate, Coeff = estimate, P = p_value, Factor = covariate, PFAS = pfas))
}

# Apply to all PFAS variables and continuous covariates
continuous_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
  bind_rows(lapply(conti_conf, function(cov) extract_model_summary(pfas, cov, data)))
}))

# Print continuous results
print(continuous_results)
##          Confounders          Coeff                   P            Factor
## 1  age_at_enrollment  0.02303963456 0.00002886969906048 age_at_enrollment
## 2                bmi -0.01294812141 0.09611802745084295               bmi
## 3        trig_mg_d_l  0.00006026119 0.92894071463283046       trig_mg_d_l
## 4  age_at_enrollment  0.01401689114 0.01151280974826796 age_at_enrollment
## 5                bmi -0.01953451965 0.01049324625441954               bmi
## 6        trig_mg_d_l -0.00087134772 0.19560135743360868       trig_mg_d_l
## 7  age_at_enrollment  0.02285242310 0.00003418692654294 age_at_enrollment
## 8                bmi -0.01016268100 0.19162630103330469               bmi
## 9        trig_mg_d_l -0.00012960498 0.84543151201780353       trig_mg_d_l
## 10 age_at_enrollment  0.03015972790 0.00000003849922364 age_at_enrollment
## 11               bmi -0.00632410823 0.39797483121906918               bmi
## 12       trig_mg_d_l -0.00060423845 0.36564464944825859       trig_mg_d_l
## 13 age_at_enrollment  0.00610856935 0.27012846280853531 age_at_enrollment
## 14               bmi -0.00353210464 0.63621285405847949               bmi
## 15       trig_mg_d_l -0.00037474559 0.57810255513241993       trig_mg_d_l
## 16 age_at_enrollment -0.00039411970 0.94418956366096030 age_at_enrollment
## 17               bmi  0.00294487490 0.67943161804413910               bmi
## 18       trig_mg_d_l -0.00051069442 0.45289086346786955       trig_mg_d_l
## 19 age_at_enrollment  0.01990379953 0.00032706545799936 age_at_enrollment
## 20               bmi -0.01850220830 0.01730193122393180               bmi
## 21       trig_mg_d_l  0.00025406806 0.70465471480120978       trig_mg_d_l
## 22 age_at_enrollment  0.00422728729 0.41715088028540981 age_at_enrollment
## 23               bmi -0.00405085209 0.60753830998090086               bmi
## 24       trig_mg_d_l -0.00053365608 0.39127771559889879       trig_mg_d_l
## 25 age_at_enrollment  0.01474075004 0.00829630670610244 age_at_enrollment
## 26               bmi -0.02788390573 0.00016089041529066               bmi
## 27       trig_mg_d_l -0.00105386273 0.11863558257777872       trig_mg_d_l
## 28 age_at_enrollment  0.03647003290 0.00000000001254972 age_at_enrollment
## 29               bmi -0.00498536122 0.49783998070122160               bmi
## 30       trig_mg_d_l  0.00035106604 0.59487932766741591       trig_mg_d_l
## 31 age_at_enrollment  0.00666942172 0.23478386028671305 age_at_enrollment
## 32               bmi -0.02691555479 0.00053014713321087               bmi
## 33       trig_mg_d_l -0.00088518107 0.19110820055049224       trig_mg_d_l
## 34 age_at_enrollment  0.00723125430 0.18396989965273475 age_at_enrollment
## 35               bmi -0.02551872568 0.00081610677927414               bmi
## 36       trig_mg_d_l -0.00011850725 0.86073672780481414       trig_mg_d_l
## 37 age_at_enrollment -0.00767217344 0.16771220778328327 age_at_enrollment
## 38               bmi  0.00258183929 0.73804930867047069               bmi
## 39       trig_mg_d_l -0.00082944038 0.21370329383697809       trig_mg_d_l
## 40 age_at_enrollment  0.00156141004 0.75969057121585326 age_at_enrollment
## 41               bmi -0.00261010632 0.73748118274195495               bmi
## 42       trig_mg_d_l -0.00015137036 0.80919075133267548       trig_mg_d_l
##            PFAS
## 1  pf_hx_s_scld
## 2  pf_hx_s_scld
## 3  pf_hx_s_scld
## 4     pfda_scld
## 5     pfda_scld
## 6     pfda_scld
## 7     pfna_scld
## 8     pfna_scld
## 9     pfna_scld
## 10    pfos_scld
## 11    pfos_scld
## 12    pfos_scld
## 13 pf_hp_a_scld
## 14 pf_hp_a_scld
## 15 pf_hp_a_scld
## 16    pfbs_scld
## 17    pfbs_scld
## 18    pfbs_scld
## 19    pfoa_scld
## 20    pfoa_scld
## 21    pfoa_scld
## 22 pf_pe_a_scld
## 23 pf_pe_a_scld
## 24 pf_pe_a_scld
## 25 pf_un_a_scld
## 26 pf_un_a_scld
## 27 pf_un_a_scld
## 28 pf_hp_s_scld
## 29 pf_hp_s_scld
## 30 pf_hp_s_scld
## 31 pf_do_a_scld
## 32 pf_do_a_scld
## 33 pf_do_a_scld
## 34 pf_pe_s_scld
## 35 pf_pe_s_scld
## 36 pf_pe_s_scld
## 37 pf_hx_a_scld
## 38 pf_hx_a_scld
## 39 pf_hx_a_scld
## 40    pfba_scld
## 41    pfba_scld
## 42    pfba_scld
# Define categorical confounders
cate_conf <- setdiff(potential_conf, conti_conf)

data <- data %>%
  mutate(across(all_of(cate_conf), as.factor))

# Function to fit model and handle categorical variables
fit_model_cat <- function(pfas, covariate, data) {
  model <- lm(as.formula(paste(pfas, "~", covariate)), data = data)
  summary_model <- summary(model)
  coefficients_df <- as.data.frame(summary_model$coefficients)
  coefficients_df <- coefficients_df[-1, ]  # Exclude intercept

  results <- data.frame()

  for (i in 1:nrow(coefficients_df)) {
    current_result <- data.frame(
      Confounders = covariate, 
      Coeff = coefficients_df[i, "Estimate"], 
      P = coefficients_df[i, "Pr(>|t|)"],
      Factor = rownames(coefficients_df)[i],
      PFAS = pfas
    )
    results <- rbind(results, current_result)
  }
  
  return(results)
}

# Apply to all PFAS variables and categorical covariates
categorical_results <- bind_rows(lapply(pfas_name_scld, function(pfas) {
  bind_rows(lapply(cate_conf, function(cov) fit_model_cat(pfas, cov, data)))
}))

# Print categorical results
print(categorical_results)
##                   Confounders        Coeff                P
## 1                      source  0.200895798 0.17816637785406
## 2                      source  0.287731004 0.02406241741592
## 3                      source -0.240398916 0.34812012829126
## 4                         sex  0.187197691 0.08219582251875
## 5              race_eth_label  0.050788492 0.84233629638901
## 6              race_eth_label -0.115104272 0.74149107535182
## 7              race_eth_label  0.274229818 0.26292213105599
## 8              race_eth_label  0.038885809 0.89888254812528
## 9            race_final_label -0.125411971 0.86037830965616
## 10           race_final_label -0.567654328 0.26261635127197
## 11           race_final_label -0.094110264 0.87184652297268
## 12           race_final_label -0.407973324 0.32604479871633
## 13           race_final_label -0.209886202 0.07137795642001
## 14           race_final_label -0.264366169 0.79275654669035
## 15           race_final_label -0.293394272 0.32387441802348
## 16           race_final_label -0.140730470 0.71483775593415
## 17                  ethnicity  0.189177168 0.43536968718843
## 18                  ethnicity  0.038885809 0.89927233218110
## 19                      rural -0.246551067 0.18530426138296
## 20                      rural -0.147877097 0.36453370981274
## 21                    smoking -0.323810274 0.07305486987873
## 22                    smoking -0.229485806 0.04908826819920
## 23           sq_drink_alcohol -0.091942208 0.55776878080577
## 24           sq_drink_alcohol -0.224849555 0.12238274703819
## 25           sq_drink_alcohol -0.005566960 0.96937817849958
## 26   sq_average_drink_per_day  0.116084716 0.65578352846414
## 27   sq_average_drink_per_day -0.261826993 0.53666952886876
## 28   sq_average_drink_per_day -0.105299660 0.40074255201137
## 29              sq_self_hep_b -0.141938612 0.21157942812386
## 30              sq_self_hep_b  0.192152070 0.44361557003896
## 31              sq_self_hep_c -0.146606869 0.19870154554602
## 32              sq_self_hep_c -0.155239261 0.52527191567200
## 33          supp_meds_tylenol  0.304940955 0.42547659640687
## 34          supp_meds_tylenol  0.419083324 0.50488516530130
## 35         supp_meds_steroids  0.040924100 0.90371151384786
## 36         supp_meds_steroids -0.657053844 0.53425913283768
## 37              sq_water_well -0.209121056 0.06225382264052
## 38              sq_water_well -0.218124485 0.15388307408752
## 39    sq_water_tap_unfiltered -0.023621223 0.86549139364515
## 40    sq_water_tap_unfiltered  0.242930596 0.06779584280842
## 41  sq_water_house_filtration -0.164768323 0.13133740209379
## 42  sq_water_house_filtration -0.015941373 0.93081104646968
## 43     sq_water_faucet_filter -0.137999833 0.25477840057300
## 44     sq_water_faucet_filter  0.058969782 0.65148799314024
## 45   sq_water_charcoal_filter -0.157835307 0.15697464465805
## 46   sq_water_charcoal_filter  0.078120988 0.61742210595572
## 47           sq_water_bottled -0.489245463 0.00049146516071
## 48           sq_water_bottled -0.425652852 0.00165296201584
## 49              sq_water_none -0.155707258 0.14497821238538
## 50              sq_water_none -0.401945941 0.10160522807343
## 51        sq_water_other_type -0.144238597 0.18408036183405
## 52        sq_water_other_type -0.171961692 0.40129154196832
## 53                     source -0.246320491 0.09903735645484
## 54                     source -0.089689998 0.48062363680237
## 55                     source -0.664123896 0.00983867718366
## 56                        sex -0.132717021 0.21831847824619
## 57             race_eth_label  0.124713526 0.62644193500231
## 58             race_eth_label  0.427845656 0.22209485749045
## 59             race_eth_label  0.219074045 0.37257706140279
## 60             race_eth_label -0.051398854 0.86705038235471
## 61           race_final_label -0.129041956 0.85548381268167
## 62           race_final_label -0.480227615 0.34013223492180
## 63           race_final_label  0.221625730 0.70227327313975
## 64           race_final_label  0.731311112 0.07689498379755
## 65           race_final_label -0.106015704 0.35862071309513
## 66           race_final_label  1.830449710 0.06776455127017
## 67           race_final_label -0.181875197 0.53812055995121
## 68           race_final_label -0.426284433 0.26576060685672
## 69                  ethnicity  0.199915847 0.40915967427517
## 70                  ethnicity -0.051398854 0.86694944980858
## 71                      rural -0.223213006 0.23079719614026
## 72                      rural -0.089919293 0.58161444509190
## 73                    smoking -0.477786491 0.00796269280740
## 74                    smoking -0.268518439 0.02067734372000
## 75           sq_drink_alcohol -0.207870279 0.17690069057810
## 76           sq_drink_alcohol -0.133574488 0.34875449247860
## 77           sq_drink_alcohol  0.343071427 0.01623256250437
## 78   sq_average_drink_per_day -0.065819563 0.79640617614151
## 79   sq_average_drink_per_day  0.309710825 0.45570924404055
## 80   sq_average_drink_per_day -0.449821686 0.00027963169345
## 81              sq_self_hep_b -0.195178728 0.08568444046719
## 82              sq_self_hep_b  0.124792657 0.61823627484439
## 83              sq_self_hep_c -0.188575571 0.09718837256862
## 84              sq_self_hep_c  0.293829902 0.22767940372033
## 85          supp_meds_tylenol -0.104426492 0.78494873278750
## 86          supp_meds_tylenol  0.137188835 0.82724883059650
## 87         supp_meds_steroids -0.209622339 0.53564564076427
## 88         supp_meds_steroids -0.222383053 0.83337724859734
## 89              sq_water_well -0.132687198 0.23787633145928
## 90              sq_water_well -0.097902158 0.52323933577829
## 91    sq_water_tap_unfiltered -0.071825166 0.60904913514096
## 92    sq_water_tap_unfiltered  0.047090239 0.72458169476826
## 93  sq_water_house_filtration -0.118485504 0.27832139702085
## 94  sq_water_house_filtration -0.069280874 0.70639209383939
## 95     sq_water_faucet_filter -0.111215023 0.35955630258943
## 96     sq_water_faucet_filter -0.003617243 0.97793718366327
## 97   sq_water_charcoal_filter -0.118284545 0.28976269305199
## 98   sq_water_charcoal_filter -0.030459708 0.84593988600084
## 99           sq_water_bottled -0.338884188 0.01623333367883
## 100          sq_water_bottled -0.326239577 0.01651137591491
## 101             sq_water_none -0.081531902 0.44683501274427
## 102             sq_water_none -0.161168516 0.51271635885510
## 103       sq_water_other_type -0.140356692 0.19632346742509
## 104       sq_water_other_type  0.005741845 0.97764091282822
## 105                    source -0.175369787 0.24029175304655
## 106                    source -0.001557516 0.99023454642434
## 107                    source -0.604201367 0.01887733932903
## 108                       sex -0.182491531 0.09022350808633
## 109            race_eth_label  0.111439098 0.66399374875135
## 110            race_eth_label  0.075383541 0.82971361421867
## 111            race_eth_label  0.235158486 0.33914499829761
## 112            race_eth_label  0.010202957 0.97352278690987
## 113          race_final_label -0.031005242 0.96535916817741
## 114          race_final_label -0.439367404 0.38636569597660
## 115          race_final_label -0.010423415 0.98576358357417
## 116          race_final_label -0.117074701 0.77822396547546
## 117          race_final_label -0.118282395 0.30947290327306
## 118          race_final_label  1.415335888 0.16063322156114
## 119          race_final_label -0.232301749 0.43522678347209
## 120          race_final_label -0.250188337 0.51661750631772
## 121                 ethnicity  0.190570814 0.43185377958703
## 122                 ethnicity  0.010202957 0.97349654239088
## 123                     rural -0.166572585 0.37123860525779
## 124                     rural -0.122277328 0.45397511461728
## 125                   smoking -0.550497166 0.00222769302932
## 126                   smoking -0.243033617 0.03579987987392
## 127          sq_drink_alcohol -0.152143531 0.32289373258877
## 128          sq_drink_alcohol -0.064578736 0.65051917779038
## 129          sq_drink_alcohol  0.395101987 0.00571563030052
## 130  sq_average_drink_per_day -0.179363348 0.48139369719177
## 131  sq_average_drink_per_day  0.339756072 0.41243345212406
## 132  sq_average_drink_per_day -0.475665430 0.00012072205421
## 133             sq_self_hep_b -0.169980812 0.13479409364304
## 134             sq_self_hep_b  0.077652727 0.75677422534222
## 135             sq_self_hep_c -0.153241706 0.17920855998162
## 136             sq_self_hep_c -0.041526636 0.86504317800144
## 137         supp_meds_tylenol  0.137831746 0.71761513785548
## 138         supp_meds_tylenol  1.045530092 0.09550981098713
## 139        supp_meds_steroids -0.308992929 0.36097388049451
## 140        supp_meds_steroids  0.101453855 0.92347692411989
## 141             sq_water_well -0.197910956 0.07794860503605
## 142             sq_water_well -0.145051446 0.34325528210842
## 143   sq_water_tap_unfiltered -0.066858483 0.63379349809383
## 144   sq_water_tap_unfiltered  0.077746821 0.56054814450977
## 145 sq_water_house_filtration -0.157196639 0.15011332667990
## 146 sq_water_house_filtration -0.063597366 0.72917960795944
## 147    sq_water_faucet_filter -0.167657482 0.16701503357658
## 148    sq_water_faucet_filter -0.062872816 0.63038370929638
## 149  sq_water_charcoal_filter -0.171482905 0.12453939373143
## 150  sq_water_charcoal_filter -0.088273734 0.57281724091754
## 151          sq_water_bottled -0.316950714 0.02481255320483
## 152          sq_water_bottled -0.257416023 0.05871655083207
## 153             sq_water_none -0.150395243 0.15986807660113
## 154             sq_water_none -0.280592971 0.25347435646347
## 155       sq_water_other_type -0.178793168 0.09958577804326
## 156       sq_water_other_type -0.063262997 0.75719660380251
## 157                    source -0.028123983 0.85071911056610
## 158                    source  0.065459503 0.60757504689612
## 159                    source -0.549343879 0.03293039529831
## 160                       sex  0.170180187 0.11421477651813
## 161            race_eth_label  0.177351427 0.48947961492581
## 162            race_eth_label  0.213688811 0.54222117666899
## 163            race_eth_label  0.220729261 0.36962368798292
## 164            race_eth_label -0.064254677 0.83445298522021
## 165          race_final_label -0.256501601 0.71719909410729
## 166          race_final_label -0.612124024 0.22393436309560
## 167          race_final_label  0.302218261 0.60200810793988
## 168          race_final_label  0.345060629 0.40285038365019
## 169          race_final_label -0.030336969 0.79256603065347
## 170          race_final_label  2.506331197 0.01250591825764
## 171          race_final_label -0.261501159 0.37590551468125
## 172          race_final_label -0.356932287 0.35102751784317
## 173                 ethnicity  0.207295824 0.39191644876826
## 174                 ethnicity -0.064254677 0.83404410390463
## 175                     rural -0.150503790 0.41945767850033
## 176                     rural -0.025677499 0.87510243365735
## 177                   smoking -0.568161472 0.00162483462452
## 178                   smoking -0.177642837 0.12484388703393
## 179          sq_drink_alcohol -0.092797903 0.55221901412800
## 180          sq_drink_alcohol -0.036172572 0.80250707464715
## 181          sq_drink_alcohol  0.250989822 0.08264764890084
## 182  sq_average_drink_per_day -0.147331304 0.56878498930872
## 183  sq_average_drink_per_day  0.137412767 0.74388410618384
## 184  sq_average_drink_per_day -0.309054336 0.01330787645467
## 185             sq_self_hep_b -0.096081724 0.39765419685309
## 186             sq_self_hep_b  0.273215641 0.27638203097615
## 187             sq_self_hep_c -0.088278490 0.43852931773111
## 188             sq_self_hep_c  0.266319451 0.27592232776750
## 189         supp_meds_tylenol  0.222254099 0.56129273508639
## 190         supp_meds_tylenol  0.490187456 0.43550301353661
## 191        supp_meds_steroids -0.001370466 0.99676971448587
## 192        supp_meds_steroids -0.083001213 0.93744563737665
## 193             sq_water_well -0.138214358 0.21869101547987
## 194             sq_water_well -0.146054748 0.34083898263585
## 195   sq_water_tap_unfiltered -0.108841289 0.43862900238366
## 196   sq_water_tap_unfiltered -0.031520281 0.81362118915628
## 197 sq_water_house_filtration -0.101441951 0.35332742478950
## 198 sq_water_house_filtration  0.028812444 0.87552600125445
## 199    sq_water_faucet_filter -0.144511349 0.23371706850435
## 200    sq_water_faucet_filter -0.115764438 0.37605746039475
## 201  sq_water_charcoal_filter -0.094357710 0.39811618261251
## 202  sq_water_charcoal_filter  0.078374853 0.61705027014983
## 203          sq_water_bottled -0.374389594 0.00776080774018
## 204          sq_water_bottled -0.398004138 0.00339692534565
## 205             sq_water_none -0.097152295 0.36444330562043
## 206             sq_water_none -0.221344056 0.36842362123646
## 207       sq_water_other_type -0.118651822 0.27482381292786
## 208       sq_water_other_type -0.128580014 0.53059508137586
## 209                    source  0.104342436 0.48142224323953
## 210                    source -0.253170703 0.04569477728083
## 211                    source  0.366718557 0.15030326977520
## 212                       sex  0.047539722 0.65952777755573
## 213            race_eth_label -0.292172691 0.24558725047750
## 214            race_eth_label  0.259040647 0.45098650537994
## 215            race_eth_label  0.197393303 0.41292192888075
## 216            race_eth_label -0.004429835 0.98826938151728
## 217          race_final_label  2.990353342 0.00001177078990
## 218          race_final_label -0.750763195 0.11711937574190
## 219          race_final_label -0.213625556 0.69833970735644
## 220          race_final_label -0.201060933 0.60825038534995
## 221          race_final_label -0.528106273 0.00000214067538
## 222          race_final_label  0.144466203 0.87920452393557
## 223          race_final_label -0.541876335 0.05422224390061
## 224          race_final_label -0.641720215 0.07841348875071
## 225                 ethnicity  0.052119485 0.82999524103982
## 226                 ethnicity -0.004429835 0.98850968884498
## 227                     rural  0.039818426 0.83089199323123
## 228                     rural  0.067323679 0.68047191960674
## 229                   smoking -0.258608572 0.15406242068696
## 230                   smoking -0.042662611 0.71521597801004
## 231          sq_drink_alcohol  0.135862805 0.38818440115655
## 232          sq_drink_alcohol  0.048099616 0.74153340009344
## 233          sq_drink_alcohol  0.065602936 0.65217578709575
## 234  sq_average_drink_per_day  0.343582611 0.18358567488179
## 235  sq_average_drink_per_day  1.133059764 0.00724023699402
## 236  sq_average_drink_per_day  0.112919552 0.36326518486072
## 237             sq_self_hep_b -0.005245175 0.96324302106208
## 238             sq_self_hep_b -0.157051829 0.53229219046350
## 239             sq_self_hep_c  0.076666646 0.50144498984546
## 240             sq_self_hep_c  0.256164632 0.29506967023342
## 241         supp_meds_tylenol  0.142366243 0.70995004114878
## 242         supp_meds_tylenol  0.095580883 0.87917492730683
## 243        supp_meds_steroids  0.106160935 0.75379520356563
## 244        supp_meds_steroids -0.006282312 0.99525978948955
## 245             sq_water_well -0.079017038 0.48242764822963
## 246             sq_water_well -0.029840353 0.84588876056705
## 247   sq_water_tap_unfiltered  0.039163075 0.78055819320376
## 248   sq_water_tap_unfiltered  0.059596919 0.65602092727267
## 249 sq_water_house_filtration -0.064832914 0.55318487403520
## 250 sq_water_house_filtration  0.044680352 0.80820617839498
## 251    sq_water_faucet_filter  0.045347249 0.70845488436321
## 252    sq_water_faucet_filter  0.153061672 0.24212063939652
## 253  sq_water_charcoal_filter -0.087033046 0.43594263519055
## 254  sq_water_charcoal_filter -0.132945406 0.39671140672906
## 255          sq_water_bottled -0.162599041 0.25016970657883
## 256          sq_water_bottled -0.218302665 0.10999177430940
## 257             sq_water_none -0.052129747 0.62687470346805
## 258             sq_water_none  0.041283762 0.86689577335487
## 259       sq_water_other_type -0.084331471 0.43780014503922
## 260       sq_water_other_type  0.059851386 0.77047252115802
## 261                    source -0.242092835 0.10550138874091
## 262                    source -0.314762573 0.01381394218896
## 263                    source -0.438709022 0.08785424567301
## 264                       sex -0.074198882 0.49160923227275
## 265            race_eth_label -0.014642162 0.95432754792565
## 266            race_eth_label -0.023386604 0.94662591167007
## 267            race_eth_label  0.216205940 0.37788131612548
## 268            race_eth_label  0.294469251 0.33681006421286
## 269          race_final_label -0.064977808 0.92729153707793
## 270          race_final_label -0.364982301 0.47055950006093
## 271          race_final_label -0.287203346 0.62210940950523
## 272          race_final_label -0.234821601 0.57120995522041
## 273          race_final_label -0.261517532 0.02465261727798
## 274          race_final_label -0.322124510 0.74855511160560
## 275          race_final_label -0.276092067 0.35256225658707
## 276          race_final_label -0.334369593 0.38492197181345
## 277                 ethnicity  0.135638436 0.57595190803645
## 278                 ethnicity  0.294469251 0.33814572280042
## 279                     rural  0.402235775 0.03046477151052
## 280                     rural -0.025462981 0.87544973896638
## 281                   smoking  0.028762081 0.87397920554473
## 282                   smoking -0.132220016 0.25890118865790
## 283          sq_drink_alcohol  0.030936089 0.84399176612941
## 284          sq_drink_alcohol -0.113543959 0.43588771007607
## 285          sq_drink_alcohol  0.042489490 0.77007949378540
## 286  sq_average_drink_per_day  0.001738499 0.99459668702332
## 287  sq_average_drink_per_day  1.434035309 0.00065732225553
## 288  sq_average_drink_per_day  0.001408930 0.99089640548375
## 289             sq_self_hep_b -0.161647304 0.15490547435844
## 290             sq_self_hep_b -0.231096311 0.35688641562230
## 291             sq_self_hep_c -0.110620492 0.33226578512771
## 292             sq_self_hep_c  0.117368598 0.63123268162753
## 293         supp_meds_tylenol  0.051114225 0.89376908860934
## 294         supp_meds_tylenol -0.100001513 0.87363945925546
## 295        supp_meds_steroids  0.067647998 0.84159227516948
## 296        supp_meds_steroids -0.135451785 0.89808056927625
## 297             sq_water_well  0.028668599 0.79878534040690
## 298             sq_water_well -0.083682739 0.58576338919295
## 299   sq_water_tap_unfiltered -0.048786994 0.72768354490861
## 300   sq_water_tap_unfiltered -0.196265781 0.14160342074545
## 301 sq_water_house_filtration  0.066166986 0.54472537125353
## 302 sq_water_house_filtration  0.183741850 0.31807308447128
## 303    sq_water_faucet_filter  0.084888011 0.48456664213754
## 304    sq_water_faucet_filter  0.059173337 0.65126163583327
## 305  sq_water_charcoal_filter  0.061960093 0.57934760076695
## 306  sq_water_charcoal_filter  0.086905004 0.57974234090502
## 307          sq_water_bottled  0.001506774 0.99150577538440
## 308          sq_water_bottled -0.113192372 0.40754821004133
## 309             sq_water_none  0.033612442 0.75393115719871
## 310             sq_water_none -0.083659173 0.73415922696711
## 311       sq_water_other_type  0.024303777 0.82315529229055
## 312       sq_water_other_type -0.047333276 0.81768277140598
## 313                    source -0.076284269 0.61053968426051
## 314                    source  0.069320160 0.58737751970846
## 315                    source -0.428567690 0.09643431102686
## 316                       sex -0.125164803 0.24571033496868
## 317            race_eth_label -0.149446479 0.55373366719946
## 318            race_eth_label  0.138177043 0.68861553399695
## 319            race_eth_label  0.298831816 0.21707705491636
## 320            race_eth_label  0.014561956 0.96159120427071
## 321          race_final_label  0.233611347 0.73943226114685
## 322          race_final_label -0.682571588 0.17165331000666
## 323          race_final_label  0.067011955 0.90716829723056
## 324          race_final_label  0.012995087 0.97464817278574
## 325          race_final_label -0.430966705 0.00019045927418
## 326          race_final_label  0.542360390 0.58429976766167
## 327          race_final_label -0.300302745 0.30534074418753
## 328          race_final_label  0.025747942 0.94588993724285
## 329                 ethnicity  0.156059572 0.51999177754676
## 330                 ethnicity  0.014561956 0.96220473628701
## 331                     rural -0.190148486 0.30702073717271
## 332                     rural -0.170630017 0.29578828788987
## 333                   smoking -0.409788834 0.02321464375482
## 334                   smoking -0.222632007 0.05574471466387
## 335          sq_drink_alcohol -0.111496089 0.46913030842951
## 336          sq_drink_alcohol -0.046732994 0.74326861358174
## 337          sq_drink_alcohol  0.409557468 0.00422640049175
## 338  sq_average_drink_per_day -0.040424653 0.87408657104043
## 339  sq_average_drink_per_day -0.260093248 0.53096833080426
## 340  sq_average_drink_per_day -0.481474629 0.00010257804470
## 341             sq_self_hep_b -0.178851753 0.11559742974388
## 342             sq_self_hep_b  0.045228192 0.85681327359605
## 343             sq_self_hep_c -0.177866607 0.11880220685087
## 344             sq_self_hep_c  0.005156484 0.98314670068817
## 345         supp_meds_tylenol  0.443801098 0.24563690847941
## 346         supp_meds_tylenol  0.770507972 0.21985255079337
## 347        supp_meds_steroids  0.107805082 0.75003621633081
## 348        supp_meds_steroids -0.380809440 0.71869901424882
## 349             sq_water_well -0.186536945 0.09675928899761
## 350             sq_water_well -0.112239398 0.46350632851723
## 351   sq_water_tap_unfiltered -0.007174993 0.95913876245625
## 352   sq_water_tap_unfiltered  0.175492994 0.18845245498274
## 353 sq_water_house_filtration -0.160648035 0.14111864812130
## 354 sq_water_house_filtration -0.184146789 0.31607063806375
## 355    sq_water_faucet_filter -0.180991093 0.13559656249832
## 356    sq_water_faucet_filter -0.144357201 0.26925087582704
## 357  sq_water_charcoal_filter -0.171264179 0.12499948414919
## 358  sq_water_charcoal_filter -0.044223355 0.77750970889268
## 359          sq_water_bottled -0.437282541 0.00182086074127
## 360          sq_water_bottled -0.459621966 0.00069540024129
## 361             sq_water_none -0.165431763 0.12150146864344
## 362             sq_water_none -0.392843913 0.10950059241962
## 363       sq_water_other_type -0.181037121 0.09537905447064
## 364       sq_water_other_type -0.037666105 0.85393082686463
## 365                    source -0.597901038 0.00005228868616
## 366                    source -0.265570259 0.03370576474755
## 367                    source  0.326611981 0.19403427471931
## 368                       sex -0.153686852 0.15386599131094
## 369            race_eth_label  0.072315457 0.77714097293777
## 370            race_eth_label  0.474258658 0.17483025510466
## 371            race_eth_label  0.086984175 0.72246058781372
## 372            race_eth_label  0.457678603 0.13554981673754
## 373          race_final_label  0.347296961 0.62303943070383
## 374          race_final_label  1.652976248 0.00107063992872
## 375          race_final_label -0.072385667 0.90034178273734
## 376          race_final_label -0.041298065 0.92002234501068
## 377          race_final_label  0.062986709 0.58422548881022
## 378          race_final_label -0.258911280 0.79509259208138
## 379          race_final_label -0.207098635 0.48201712804661
## 380          race_final_label -0.227823677 0.55058320024782
## 381                 ethnicity  0.099940992 0.67927159271312
## 382                 ethnicity  0.457678603 0.13566211156982
## 383                     rural -0.147231442 0.42822635251512
## 384                     rural -0.260727617 0.11000532282996
## 385                   smoking  0.216112013 0.23338823688727
## 386                   smoking -0.066187762 0.57137978178832
## 387          sq_drink_alcohol  0.101568743 0.51822776011725
## 388          sq_drink_alcohol -0.001902538 0.98957692131135
## 389          sq_drink_alcohol  0.151438614 0.29776677122813
## 390  sq_average_drink_per_day  0.057487391 0.82519940509489
## 391  sq_average_drink_per_day -0.344924347 0.41554963544340
## 392  sq_average_drink_per_day -0.132431733 0.29051139953655
## 393             sq_self_hep_b -0.127726808 0.26138986317014
## 394             sq_self_hep_b -0.189266149 0.45099558920554
## 395             sq_self_hep_c -0.124364032 0.27562127705554
## 396             sq_self_hep_c -0.191670281 0.43308498300754
## 397         supp_meds_tylenol  0.259503283 0.49765332661943
## 398         supp_meds_tylenol  0.077719005 0.90156876480205
## 399        supp_meds_steroids  0.217854838 0.51959775927010
## 400        supp_meds_steroids -0.290150953 0.78364104048900
## 401             sq_water_well -0.035396466 0.75308976935880
## 402             sq_water_well -0.004037423 0.97903005717147
## 403   sq_water_tap_unfiltered -0.227639599 0.10460564698569
## 404   sq_water_tap_unfiltered -0.209658682 0.11625097875513
## 405 sq_water_house_filtration  0.031800030 0.76900375265055
## 406 sq_water_house_filtration  0.499637112 0.00639538693209
## 407    sq_water_faucet_filter -0.007319188 0.95193128359767
## 408    sq_water_faucet_filter  0.073005971 0.57712410414268
## 409  sq_water_charcoal_filter -0.026450287 0.81297502605541
## 410  sq_water_charcoal_filter  0.038558107 0.80596269263471
## 411          sq_water_bottled -0.128076641 0.36598943670439
## 412          sq_water_bottled -0.100027498 0.46439951786495
## 413             sq_water_none -0.001346337 0.98995994083385
## 414             sq_water_none -0.333071106 0.17591982687334
## 415       sq_water_other_type  0.029917853 0.78311655900997
## 416       sq_water_other_type  0.149328289 0.46695697882131
## 417                    source -0.122335675 0.40741994614468
## 418                    source  0.166651022 0.18606871857654
## 419                    source -0.710857596 0.00529967763418
## 420                       sex -0.081072838 0.45234435016076
## 421            race_eth_label  0.388436412 0.12719466758624
## 422            race_eth_label  0.754963047 0.03031047612560
## 423            race_eth_label  0.309387448 0.20479634375197
## 424            race_eth_label -0.069642049 0.81923914578154
## 425          race_final_label -0.542170032 0.43999328372661
## 426          race_final_label -0.494735976 0.32117620165951
## 427          race_final_label  0.319841767 0.57762843834789
## 428          race_final_label  1.390329246 0.00073512345399
## 429          race_final_label  0.081404362 0.47660773266545
## 430          race_final_label  0.533002293 0.59053852024601
## 431          race_final_label -0.225942878 0.44010936421998
## 432          race_final_label -0.560240155 0.14005245576398
## 433                 ethnicity  0.353302356 0.14289994452075
## 434                 ethnicity -0.069642049 0.81947623415182
## 435                     rural -0.210790739 0.25793964453799
## 436                     rural -0.047194429 0.77247965322541
## 437                   smoking -0.559380006 0.00181874007454
## 438                   smoking -0.320177642 0.00560773476027
## 439          sq_drink_alcohol -0.177862327 0.24777442714208
## 440          sq_drink_alcohol -0.166639376 0.24258750496615
## 441          sq_drink_alcohol  0.340041634 0.01719459843916
## 442  sq_average_drink_per_day  0.167092869 0.51266621906040
## 443  sq_average_drink_per_day  0.035136353 0.93254748917460
## 444  sq_average_drink_per_day -0.427661820 0.00054676111079
## 445             sq_self_hep_b -0.227855130 0.04436660508740
## 446             sq_self_hep_b  0.243875526 0.32895313970868
## 447             sq_self_hep_c -0.231106095 0.04207291408647
## 448             sq_self_hep_c  0.220640012 0.36434293822168
## 449         supp_meds_tylenol -0.324432302 0.39565811530909
## 450         supp_meds_tylenol  0.265279446 0.67233383417726
## 451        supp_meds_steroids -0.436901492 0.19622409137501
## 452        supp_meds_steroids -0.021862618 0.98346705424688
## 453             sq_water_well -0.128171207 0.25427262322351
## 454             sq_water_well -0.024950983 0.87074988658916
## 455   sq_water_tap_unfiltered -0.222055106 0.11348990546405
## 456   sq_water_tap_unfiltered -0.070017536 0.59948647097833
## 457 sq_water_house_filtration -0.132638341 0.22481218952721
## 458 sq_water_house_filtration -0.039782055 0.82866854646979
## 459    sq_water_faucet_filter -0.131448800 0.27842603953197
## 460    sq_water_faucet_filter  0.029760120 0.81982321014451
## 461  sq_water_charcoal_filter -0.114798173 0.30392858519815
## 462  sq_water_charcoal_filter  0.052864139 0.73582611255727
## 463          sq_water_bottled -0.351141936 0.01283108050418
## 464          sq_water_bottled -0.281397807 0.03857335714817
## 465             sq_water_none -0.096455729 0.36794161779746
## 466             sq_water_none -0.209125261 0.39547290349843
## 467       sq_water_other_type -0.139208496 0.20007125981871
## 468       sq_water_other_type -0.078118811 0.70304722582391
## 469                    source  0.061680049 0.67844612713382
## 470                    source  0.228325732 0.07252736028874
## 471                    source -0.456481340 0.07474208092999
## 472                       sex  0.303500510 0.00469493718684
## 473            race_eth_label  0.171448227 0.50197497862571
## 474            race_eth_label -0.082866147 0.81223970154000
## 475            race_eth_label  0.359143079 0.14277455985686
## 476            race_eth_label  0.170940815 0.57643705503039
## 477          race_final_label -0.355957031 0.61712705883957
## 478          race_final_label -0.759880711 0.13339352193114
## 479          race_final_label -0.063258723 0.91352122741302
## 480          race_final_label -0.375470252 0.36528543014309
## 481          race_final_label -0.167859238 0.14835335109304
## 482          race_final_label  1.008017086 0.31601277293424
## 483          race_final_label -0.254488284 0.39135877967639
## 484          race_final_label -0.272328943 0.47898862867844
## 485                 ethnicity  0.282534341 0.24394937568137
## 486                 ethnicity  0.170940815 0.57772753504936
## 487                     rural -0.153308591 0.41080517756178
## 488                     rural -0.052872710 0.74619014229260
## 489                   smoking -0.597431528 0.00092276549008
## 490                   smoking -0.121792259 0.29205421769069
## 491          sq_drink_alcohol -0.065354458 0.67660542380994
## 492          sq_drink_alcohol  0.011750535 0.93549031572168
## 493          sq_drink_alcohol  0.208068661 0.15152091369329
## 494  sq_average_drink_per_day  0.015959027 0.95098333148844
## 495  sq_average_drink_per_day  0.063766417 0.88000354530749
## 496  sq_average_drink_per_day -0.216089727 0.08417452030593
## 497             sq_self_hep_b -0.022472789 0.84347580470059
## 498             sq_self_hep_b  0.146603021 0.55989141502025
## 499             sq_self_hep_c -0.028534432 0.80272136125276
## 500             sq_self_hep_c  0.026712887 0.91313950657492
## 501         supp_meds_tylenol  0.302693492 0.42836384684072
## 502         supp_meds_tylenol  0.781438896 0.21353816457676
## 503        supp_meds_steroids -0.105591434 0.75503614022517
## 504        supp_meds_steroids -0.466254373 0.65925938965953
## 505             sq_water_well -0.067772539 0.54651262733605
## 506             sq_water_well -0.155400137 0.31136152736213
## 507   sq_water_tap_unfiltered -0.007275703 0.95872659269935
## 508   sq_water_tap_unfiltered  0.026032274 0.84574417416999
## 509 sq_water_house_filtration -0.036057060 0.74164992928475
## 510 sq_water_house_filtration -0.016420265 0.92895252444545
## 511    sq_water_faucet_filter -0.078388895 0.51835547757239
## 512    sq_water_faucet_filter -0.124962156 0.33973975540600
## 513  sq_water_charcoal_filter -0.042810377 0.70176869616977
## 514  sq_water_charcoal_filter  0.024256103 0.87718241871529
## 515          sq_water_bottled -0.343434053 0.01441692733044
## 516          sq_water_bottled -0.433131801 0.00143238137611
## 517             sq_water_none -0.039586203 0.71147423698222
## 518             sq_water_none -0.320453635 0.19292650115117
## 519       sq_water_other_type -0.044492329 0.68223041086397
## 520       sq_water_other_type -0.166568984 0.41705478475211
## 521                    source  0.003415976 0.98179146397575
## 522                    source  0.045020692 0.72434393181778
## 523                    source -0.512003703 0.04708117779278
## 524                       sex -0.023593908 0.82693773382376
## 525            race_eth_label  0.060863687 0.81187103175203
## 526            race_eth_label  0.283568579 0.41725220517750
## 527            race_eth_label  0.170801740 0.48604179615838
## 528            race_eth_label -0.239941118 0.43384263785675
## 529          race_final_label -0.462599423 0.51535911876491
## 530          race_final_label -0.486996060 0.33501616397590
## 531          race_final_label  0.006627957 0.99090900750855
## 532          race_final_label  0.762330953 0.06613992103282
## 533          race_final_label -0.107573068 0.35324429414613
## 534          race_final_label -0.151427581 0.88002788778056
## 535          race_final_label -0.076316098 0.79678214569844
## 536          race_final_label -0.540407962 0.15992207734304
## 537                 ethnicity  0.142621379 0.55500838863838
## 538                 ethnicity -0.239941118 0.43331808561261
## 539                     rural -0.132863570 0.47604978557856
## 540                     rural  0.004063870 0.98015694799679
## 541                   smoking -0.339253335 0.06089981157603
## 542                   smoking -0.160431500 0.16912075431888
## 543          sq_drink_alcohol  0.033696841 0.82844187593956
## 544          sq_drink_alcohol  0.039289037 0.78511449567258
## 545          sq_drink_alcohol  0.377990514 0.00887878150753
## 546  sq_average_drink_per_day -0.146498661 0.56947537328378
## 547  sq_average_drink_per_day -0.006154150 0.98827941703331
## 548  sq_average_drink_per_day -0.377444112 0.00245565678436
## 549             sq_self_hep_b -0.141051588 0.21493465638587
## 550             sq_self_hep_b  0.042106924 0.86677177612830
## 551             sq_self_hep_c -0.099793531 0.37976264387495
## 552             sq_self_hep_c  0.428406768 0.07911871955042
## 553         supp_meds_tylenol -0.241743315 0.52552310304422
## 554         supp_meds_tylenol  0.743002153 0.23522327501678
## 555        supp_meds_steroids -0.218523659 0.51797858027497
## 556        supp_meds_steroids  0.714542748 0.49868186690490
## 557             sq_water_well  0.011686060 0.91721458430984
## 558             sq_water_well  0.126086020 0.41156166219780
## 559   sq_water_tap_unfiltered -0.092200494 0.51184853848188
## 560   sq_water_tap_unfiltered -0.075435399 0.57279182355582
## 561 sq_water_house_filtration -0.011928675 0.91310090818640
## 562 sq_water_house_filtration -0.129770756 0.48093371950682
## 563    sq_water_faucet_filter -0.027285058 0.82219076468471
## 564    sq_water_faucet_filter -0.090228579 0.49076539977289
## 565  sq_water_charcoal_filter  0.036827190 0.74186846476578
## 566  sq_water_charcoal_filter  0.040403136 0.79688719547919
## 567          sq_water_bottled -0.177736099 0.20792533592252
## 568          sq_water_bottled -0.275383208 0.04357849187013
## 569             sq_water_none  0.022297708 0.83524455521135
## 570             sq_water_none -0.129187361 0.59997849432490
## 571       sq_water_other_type -0.060323288 0.57889504928244
## 572       sq_water_other_type  0.092365555 0.65262891764172
## 573                    source  0.128461465 0.38687468700135
## 574                    source  0.397209096 0.00181891699049
## 575                    source  0.041555547 0.87053458291734
## 576                       sex  0.081196520 0.45165441374254
## 577            race_eth_label -0.005861819 0.98166500714231
## 578            race_eth_label  0.072383603 0.83548591495917
## 579            race_eth_label  0.279024063 0.25418843077719
## 580            race_eth_label  0.047160447 0.87739425723636
## 581          race_final_label  1.360287326 0.05400990074290
## 582          race_final_label -0.677922478 0.17574960546630
## 583          race_final_label -0.050667294 0.92993187406726
## 584          race_final_label -0.433484494 0.29078243295854
## 585          race_final_label -0.304808265 0.00818424840147
## 586          race_final_label  0.211835891 0.83120895797992
## 587          race_final_label -0.366394274 0.21245914434555
## 588          race_final_label -0.371598373 0.32895717410029
## 589                 ethnicity  0.183595676 0.44910052978292
## 590                 ethnicity  0.047160447 0.87801175679233
## 591                     rural -0.293698353 0.11397432465806
## 592                     rural -0.225951890 0.16528201759410
## 593                   smoking -0.328121339 0.07025007004598
## 594                   smoking -0.086475186 0.45886310938747
## 595          sq_drink_alcohol  0.008721390 0.95557605731021
## 596          sq_drink_alcohol  0.053297528 0.71336276014425
## 597          sq_drink_alcohol  0.264186688 0.06864932704253
## 598  sq_average_drink_per_day  0.345986259 0.18167914444427
## 599  sq_average_drink_per_day -0.077065092 0.85474769829995
## 600  sq_average_drink_per_day -0.187162580 0.13320907616193
## 601             sq_self_hep_b -0.043841269 0.70010452722554
## 602             sq_self_hep_b -0.148523564 0.55475236942286
## 603             sq_self_hep_c -0.008504837 0.94062361844989
## 604             sq_self_hep_c -0.133253026 0.58632012495494
## 605         supp_meds_tylenol  0.181507556 0.63535705748588
## 606         supp_meds_tylenol  0.167413591 0.79002340503006
## 607        supp_meds_steroids  0.162567452 0.63085743718607
## 608        supp_meds_steroids -0.402999024 0.70298916766313
## 609             sq_water_well -0.031775990 0.77734871293318
## 610             sq_water_well -0.161526945 0.29269446086610
## 611   sq_water_tap_unfiltered  0.130887263 0.34954163790135
## 612   sq_water_tap_unfiltered  0.265440810 0.04667168825859
## 613 sq_water_house_filtration  0.035397161 0.74609488302110
## 614 sq_water_house_filtration  0.122866958 0.50458013295510
## 615    sq_water_faucet_filter  0.074224982 0.54112145438895
## 616    sq_water_faucet_filter  0.046001885 0.72532178061484
## 617  sq_water_charcoal_filter -0.017015905 0.87904866822130
## 618  sq_water_charcoal_filter -0.007147878 0.96368572588700
## 619          sq_water_bottled -0.303379520 0.02943808106098
## 620          sq_water_bottled -0.527135357 0.00009910794049
## 621             sq_water_none  0.032209065 0.76347440250853
## 622             sq_water_none -0.286379175 0.24456352714546
## 623       sq_water_other_type -0.000698204 0.99487761581831
## 624       sq_water_other_type  0.047675534 0.81640595739470
## 625                    source  0.518825224 0.00035296753017
## 626                    source  0.273293280 0.02652537691163
## 627                    source  1.365624166 0.00000006218287
## 628                       sex -0.152564255 0.15690319949755
## 629            race_eth_label -0.099619137 0.69832721067773
## 630            race_eth_label -0.280687329 0.42434022335422
## 631            race_eth_label -0.155025713 0.52926880834616
## 632            race_eth_label -0.240179832 0.43570815119400
## 633          race_final_label  0.363145761 0.60936886944373
## 634          race_final_label -0.125495861 0.80359047025395
## 635          race_final_label  0.085954218 0.88247237807533
## 636          race_final_label -0.557711314 0.17818156233011
## 637          race_final_label  0.057141508 0.62161421926663
## 638          race_final_label  2.154326215 0.03221506802430
## 639          race_final_label -0.132608948 0.65440808798253
## 640          race_final_label  0.322560344 0.40092858744280
## 641                 ethnicity -0.143914516 0.55309406174543
## 642                 ethnicity -0.240179832 0.43478718661802
## 643                     rural -0.030728063 0.86877802201808
## 644                     rural  0.227249815 0.16396371383201
## 645                   smoking  0.383823460 0.03156290235867
## 646                   smoking  0.424432242 0.00024963802757
## 647          sq_drink_alcohol -0.106510076 0.49296709044419
## 648          sq_drink_alcohol  0.296946616 0.03963369285390
## 649          sq_drink_alcohol -0.085498716 0.55172036530078
## 650  sq_average_drink_per_day -0.104427621 0.68793945814010
## 651  sq_average_drink_per_day -0.273190945 0.51845922867124
## 652  sq_average_drink_per_day  0.139826105 0.26388573205593
## 653             sq_self_hep_b  0.338679517 0.00270288931959
## 654             sq_self_hep_b -0.238220118 0.33688740607012
## 655             sq_self_hep_c  0.386303960 0.00064482875449
## 656             sq_self_hep_c  0.339735501 0.15897129077816
## 657         supp_meds_tylenol -0.039791052 0.91717244608162
## 658         supp_meds_tylenol -0.354387040 0.57295049357703
## 659        supp_meds_steroids  0.173481308 0.60813457682163
## 660        supp_meds_steroids  0.657077748 0.53424295732900
## 661             sq_water_well  0.284585889 0.01091883956148
## 662             sq_water_well -0.056920712 0.70803138909272
## 663   sq_water_tap_unfiltered  0.372682966 0.00767897644492
## 664   sq_water_tap_unfiltered  0.132440345 0.31755980881214
## 665 sq_water_house_filtration  0.276526321 0.01100469577270
## 666 sq_water_house_filtration -0.062346803 0.73243980970183
## 667    sq_water_faucet_filter  0.331222398 0.00611511609349
## 668    sq_water_faucet_filter  0.096415171 0.45709065759293
## 669  sq_water_charcoal_filter  0.289475271 0.00910866204876
## 670  sq_water_charcoal_filter -0.085752388 0.58051667684540
## 671          sq_water_bottled  0.329643039 0.01932347445562
## 672          sq_water_bottled  0.069930043 0.60590779765293
## 673             sq_water_none  0.279475921 0.00867219569147
## 674             sq_water_none -0.229269416 0.34669199788674
## 675       sq_water_other_type  0.337214768 0.00169010311525
## 676       sq_water_other_type  0.565255198 0.00524748730282
## 677                    source -0.506598992 0.00049247622953
## 678                    source -0.713318581 0.00000001380182
## 679                    source -0.676877491 0.00655977315887
## 680                       sex  0.319078694 0.00293897166878
## 681            race_eth_label -0.384561281 0.13013569457742
## 682            race_eth_label -0.015459945 0.96442794455499
## 683            race_eth_label -0.177342612 0.46598140195592
## 684            race_eth_label  0.282335829 0.35335867587746
## 685          race_final_label  1.135611694 0.10806768285307
## 686          race_final_label -0.425309066 0.39610942244147
## 687          race_final_label -0.471448517 0.41428300467105
## 688          race_final_label  0.306275468 0.45604501729654
## 689          race_final_label -0.307432538 0.00776078864512
## 690          race_final_label -0.471448517 0.63586430273092
## 691          race_final_label -0.420749965 0.15305394925531
## 692          race_final_label -0.167562266 0.66018676901763
## 693                 ethnicity -0.232734365 0.33347886020211
## 694                 ethnicity  0.282335829 0.35448966539951
## 695                     rural -0.017570768 0.92489389560955
## 696                     rural -0.109517642 0.50284536393231
## 697                   smoking -0.179856476 0.32177091020604
## 698                   smoking -0.072533826 0.53558707695853
## 699          sq_drink_alcohol  0.327969771 0.03594188193303
## 700          sq_drink_alcohol  0.024728206 0.86408706078347
## 701          sq_drink_alcohol -0.067189872 0.64121543008338
## 702  sq_average_drink_per_day -0.270412395 0.29799235545930
## 703  sq_average_drink_per_day  0.222094164 0.59912099850931
## 704  sq_average_drink_per_day  0.139006224 0.26608530903584
## 705             sq_self_hep_b -0.080549601 0.47899561405699
## 706             sq_self_hep_b  0.123863430 0.62208712770531
## 707             sq_self_hep_c -0.033324941 0.77046630225326
## 708             sq_self_hep_c -0.013533496 0.95592885498328
## 709         supp_meds_tylenol  0.074785778 0.84501740098413
## 710         supp_meds_tylenol -0.270640942 0.66676216096643
## 711        supp_meds_steroids  0.146786904 0.66450267863285
## 712        supp_meds_steroids -0.055286439 0.95829689341887
## 713             sq_water_well -0.071131278 0.52721094260777
## 714             sq_water_well -0.020271407 0.89496451658351
## 715   sq_water_tap_unfiltered -0.081146477 0.56352426626744
## 716   sq_water_tap_unfiltered -0.119942521 0.36983338632043
## 717 sq_water_house_filtration -0.029969350 0.78307622374691
## 718 sq_water_house_filtration  0.322655831 0.07896650237649
## 719    sq_water_faucet_filter  0.098297135 0.41138610856269
## 720    sq_water_faucet_filter  0.429929406 0.00093424336604
## 721  sq_water_charcoal_filter -0.120332622 0.27988058283521
## 722  sq_water_charcoal_filter -0.289534580 0.06445662344530
## 723          sq_water_bottled -0.103467380 0.46520637575138
## 724          sq_water_bottled -0.107528668 0.43170112368954
## 725             sq_water_none -0.018050259 0.86604968749351
## 726             sq_water_none  0.311629483 0.20541041566927
## 727       sq_water_other_type -0.069122669 0.52440830804744
## 728       sq_water_other_type -0.229725344 0.26277924482141
##                                                   Factor         PFAS
## 1                                             sourceDUKE pf_hx_s_scld
## 2                                             sourceNCSU pf_hx_s_scld
## 3                                              sourceUNC pf_hx_s_scld
## 4                                                sexMale pf_hx_s_scld
## 5                                      race_eth_labelNHB pf_hx_s_scld
## 6                                      race_eth_labelNHO pf_hx_s_scld
## 7                                      race_eth_labelNHW pf_hx_s_scld
## 8                     race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 9                        race_final_labelAmerican Indian pf_hx_s_scld
## 10        race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 11                                 race_final_labelAsian pf_hx_s_scld
## 12                race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 13                                 race_final_labelBlack pf_hx_s_scld
## 14                    race_final_labelMore than one race pf_hx_s_scld
## 15                                 race_final_labelOther pf_hx_s_scld
## 16                  race_final_labelUnknown/Not Reported pf_hx_s_scld
## 17                                 ethnicityNot Hispanic pf_hx_s_scld
## 18                         ethnicityUnknown/Not Reported pf_hx_s_scld
## 19                             ruralLiving in rural area pf_hx_s_scld
## 20                             ruralUnknown/Not Reported pf_hx_s_scld
## 21                              smokingSmoke or use vape pf_hx_s_scld
## 22                           smokingUnknown/Not Reported pf_hx_s_scld
## 23          sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 24                  sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 25                  sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 26  sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 27  sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 28          sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 29                     sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 30                                      sq_self_hep_bYes pf_hx_s_scld
## 31                     sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 32                                      sq_self_hep_cYes pf_hx_s_scld
## 33                 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 34                                  supp_meds_tylenolYes pf_hx_s_scld
## 35                supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 36                                 supp_meds_steroidsYes pf_hx_s_scld
## 37                     sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 38                                      sq_water_wellYes pf_hx_s_scld
## 39           sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 40                            sq_water_tap_unfilteredYes pf_hx_s_scld
## 41         sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 42                          sq_water_house_filtrationYes pf_hx_s_scld
## 43            sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 44                             sq_water_faucet_filterYes pf_hx_s_scld
## 45          sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 46                           sq_water_charcoal_filterYes pf_hx_s_scld
## 47                  sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 48                                   sq_water_bottledYes pf_hx_s_scld
## 49                     sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 50                                      sq_water_noneYes pf_hx_s_scld
## 51               sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 52                                sq_water_other_typeYes pf_hx_s_scld
## 53                                            sourceDUKE    pfda_scld
## 54                                            sourceNCSU    pfda_scld
## 55                                             sourceUNC    pfda_scld
## 56                                               sexMale    pfda_scld
## 57                                     race_eth_labelNHB    pfda_scld
## 58                                     race_eth_labelNHO    pfda_scld
## 59                                     race_eth_labelNHW    pfda_scld
## 60                    race_eth_labelUnknown/Not Reported    pfda_scld
## 61                       race_final_labelAmerican Indian    pfda_scld
## 62        race_final_labelAmerican Indian/Alaskan Native    pfda_scld
## 63                                 race_final_labelAsian    pfda_scld
## 64                race_final_labelAsian/Pacific Islander    pfda_scld
## 65                                 race_final_labelBlack    pfda_scld
## 66                    race_final_labelMore than one race    pfda_scld
## 67                                 race_final_labelOther    pfda_scld
## 68                  race_final_labelUnknown/Not Reported    pfda_scld
## 69                                 ethnicityNot Hispanic    pfda_scld
## 70                         ethnicityUnknown/Not Reported    pfda_scld
## 71                             ruralLiving in rural area    pfda_scld
## 72                             ruralUnknown/Not Reported    pfda_scld
## 73                              smokingSmoke or use vape    pfda_scld
## 74                           smokingUnknown/Not Reported    pfda_scld
## 75          sq_drink_alcoholNo, former drinker (stopped)    pfda_scld
## 76                  sq_drink_alcoholUnknown/Not Reported    pfda_scld
## 77                  sq_drink_alcoholYes, current drinker    pfda_scld
## 78  sq_average_drink_per_day1-2 alcoholic drinks per day    pfda_scld
## 79  sq_average_drink_per_day3-4 alcoholic drinks per day    pfda_scld
## 80          sq_average_drink_per_dayUnknown/Not Reported    pfda_scld
## 81                     sq_self_hep_bUnknown/Not Reported    pfda_scld
## 82                                      sq_self_hep_bYes    pfda_scld
## 83                     sq_self_hep_cUnknown/Not Reported    pfda_scld
## 84                                      sq_self_hep_cYes    pfda_scld
## 85                 supp_meds_tylenolUnknown/Not Reported    pfda_scld
## 86                                  supp_meds_tylenolYes    pfda_scld
## 87                supp_meds_steroidsUnknown/Not Reported    pfda_scld
## 88                                 supp_meds_steroidsYes    pfda_scld
## 89                     sq_water_wellUnknown/Not Reported    pfda_scld
## 90                                      sq_water_wellYes    pfda_scld
## 91           sq_water_tap_unfilteredUnknown/Not Reported    pfda_scld
## 92                            sq_water_tap_unfilteredYes    pfda_scld
## 93         sq_water_house_filtrationUnknown/Not Reported    pfda_scld
## 94                          sq_water_house_filtrationYes    pfda_scld
## 95            sq_water_faucet_filterUnknown/Not Reported    pfda_scld
## 96                             sq_water_faucet_filterYes    pfda_scld
## 97          sq_water_charcoal_filterUnknown/Not Reported    pfda_scld
## 98                           sq_water_charcoal_filterYes    pfda_scld
## 99                  sq_water_bottledUnknown/Not Reported    pfda_scld
## 100                                  sq_water_bottledYes    pfda_scld
## 101                    sq_water_noneUnknown/Not Reported    pfda_scld
## 102                                     sq_water_noneYes    pfda_scld
## 103              sq_water_other_typeUnknown/Not Reported    pfda_scld
## 104                               sq_water_other_typeYes    pfda_scld
## 105                                           sourceDUKE    pfna_scld
## 106                                           sourceNCSU    pfna_scld
## 107                                            sourceUNC    pfna_scld
## 108                                              sexMale    pfna_scld
## 109                                    race_eth_labelNHB    pfna_scld
## 110                                    race_eth_labelNHO    pfna_scld
## 111                                    race_eth_labelNHW    pfna_scld
## 112                   race_eth_labelUnknown/Not Reported    pfna_scld
## 113                      race_final_labelAmerican Indian    pfna_scld
## 114       race_final_labelAmerican Indian/Alaskan Native    pfna_scld
## 115                                race_final_labelAsian    pfna_scld
## 116               race_final_labelAsian/Pacific Islander    pfna_scld
## 117                                race_final_labelBlack    pfna_scld
## 118                   race_final_labelMore than one race    pfna_scld
## 119                                race_final_labelOther    pfna_scld
## 120                 race_final_labelUnknown/Not Reported    pfna_scld
## 121                                ethnicityNot Hispanic    pfna_scld
## 122                        ethnicityUnknown/Not Reported    pfna_scld
## 123                            ruralLiving in rural area    pfna_scld
## 124                            ruralUnknown/Not Reported    pfna_scld
## 125                             smokingSmoke or use vape    pfna_scld
## 126                          smokingUnknown/Not Reported    pfna_scld
## 127         sq_drink_alcoholNo, former drinker (stopped)    pfna_scld
## 128                 sq_drink_alcoholUnknown/Not Reported    pfna_scld
## 129                 sq_drink_alcoholYes, current drinker    pfna_scld
## 130 sq_average_drink_per_day1-2 alcoholic drinks per day    pfna_scld
## 131 sq_average_drink_per_day3-4 alcoholic drinks per day    pfna_scld
## 132         sq_average_drink_per_dayUnknown/Not Reported    pfna_scld
## 133                    sq_self_hep_bUnknown/Not Reported    pfna_scld
## 134                                     sq_self_hep_bYes    pfna_scld
## 135                    sq_self_hep_cUnknown/Not Reported    pfna_scld
## 136                                     sq_self_hep_cYes    pfna_scld
## 137                supp_meds_tylenolUnknown/Not Reported    pfna_scld
## 138                                 supp_meds_tylenolYes    pfna_scld
## 139               supp_meds_steroidsUnknown/Not Reported    pfna_scld
## 140                                supp_meds_steroidsYes    pfna_scld
## 141                    sq_water_wellUnknown/Not Reported    pfna_scld
## 142                                     sq_water_wellYes    pfna_scld
## 143          sq_water_tap_unfilteredUnknown/Not Reported    pfna_scld
## 144                           sq_water_tap_unfilteredYes    pfna_scld
## 145        sq_water_house_filtrationUnknown/Not Reported    pfna_scld
## 146                         sq_water_house_filtrationYes    pfna_scld
## 147           sq_water_faucet_filterUnknown/Not Reported    pfna_scld
## 148                            sq_water_faucet_filterYes    pfna_scld
## 149         sq_water_charcoal_filterUnknown/Not Reported    pfna_scld
## 150                          sq_water_charcoal_filterYes    pfna_scld
## 151                 sq_water_bottledUnknown/Not Reported    pfna_scld
## 152                                  sq_water_bottledYes    pfna_scld
## 153                    sq_water_noneUnknown/Not Reported    pfna_scld
## 154                                     sq_water_noneYes    pfna_scld
## 155              sq_water_other_typeUnknown/Not Reported    pfna_scld
## 156                               sq_water_other_typeYes    pfna_scld
## 157                                           sourceDUKE    pfos_scld
## 158                                           sourceNCSU    pfos_scld
## 159                                            sourceUNC    pfos_scld
## 160                                              sexMale    pfos_scld
## 161                                    race_eth_labelNHB    pfos_scld
## 162                                    race_eth_labelNHO    pfos_scld
## 163                                    race_eth_labelNHW    pfos_scld
## 164                   race_eth_labelUnknown/Not Reported    pfos_scld
## 165                      race_final_labelAmerican Indian    pfos_scld
## 166       race_final_labelAmerican Indian/Alaskan Native    pfos_scld
## 167                                race_final_labelAsian    pfos_scld
## 168               race_final_labelAsian/Pacific Islander    pfos_scld
## 169                                race_final_labelBlack    pfos_scld
## 170                   race_final_labelMore than one race    pfos_scld
## 171                                race_final_labelOther    pfos_scld
## 172                 race_final_labelUnknown/Not Reported    pfos_scld
## 173                                ethnicityNot Hispanic    pfos_scld
## 174                        ethnicityUnknown/Not Reported    pfos_scld
## 175                            ruralLiving in rural area    pfos_scld
## 176                            ruralUnknown/Not Reported    pfos_scld
## 177                             smokingSmoke or use vape    pfos_scld
## 178                          smokingUnknown/Not Reported    pfos_scld
## 179         sq_drink_alcoholNo, former drinker (stopped)    pfos_scld
## 180                 sq_drink_alcoholUnknown/Not Reported    pfos_scld
## 181                 sq_drink_alcoholYes, current drinker    pfos_scld
## 182 sq_average_drink_per_day1-2 alcoholic drinks per day    pfos_scld
## 183 sq_average_drink_per_day3-4 alcoholic drinks per day    pfos_scld
## 184         sq_average_drink_per_dayUnknown/Not Reported    pfos_scld
## 185                    sq_self_hep_bUnknown/Not Reported    pfos_scld
## 186                                     sq_self_hep_bYes    pfos_scld
## 187                    sq_self_hep_cUnknown/Not Reported    pfos_scld
## 188                                     sq_self_hep_cYes    pfos_scld
## 189                supp_meds_tylenolUnknown/Not Reported    pfos_scld
## 190                                 supp_meds_tylenolYes    pfos_scld
## 191               supp_meds_steroidsUnknown/Not Reported    pfos_scld
## 192                                supp_meds_steroidsYes    pfos_scld
## 193                    sq_water_wellUnknown/Not Reported    pfos_scld
## 194                                     sq_water_wellYes    pfos_scld
## 195          sq_water_tap_unfilteredUnknown/Not Reported    pfos_scld
## 196                           sq_water_tap_unfilteredYes    pfos_scld
## 197        sq_water_house_filtrationUnknown/Not Reported    pfos_scld
## 198                         sq_water_house_filtrationYes    pfos_scld
## 199           sq_water_faucet_filterUnknown/Not Reported    pfos_scld
## 200                            sq_water_faucet_filterYes    pfos_scld
## 201         sq_water_charcoal_filterUnknown/Not Reported    pfos_scld
## 202                          sq_water_charcoal_filterYes    pfos_scld
## 203                 sq_water_bottledUnknown/Not Reported    pfos_scld
## 204                                  sq_water_bottledYes    pfos_scld
## 205                    sq_water_noneUnknown/Not Reported    pfos_scld
## 206                                     sq_water_noneYes    pfos_scld
## 207              sq_water_other_typeUnknown/Not Reported    pfos_scld
## 208                               sq_water_other_typeYes    pfos_scld
## 209                                           sourceDUKE pf_hp_a_scld
## 210                                           sourceNCSU pf_hp_a_scld
## 211                                            sourceUNC pf_hp_a_scld
## 212                                              sexMale pf_hp_a_scld
## 213                                    race_eth_labelNHB pf_hp_a_scld
## 214                                    race_eth_labelNHO pf_hp_a_scld
## 215                                    race_eth_labelNHW pf_hp_a_scld
## 216                   race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 217                      race_final_labelAmerican Indian pf_hp_a_scld
## 218       race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 219                                race_final_labelAsian pf_hp_a_scld
## 220               race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 221                                race_final_labelBlack pf_hp_a_scld
## 222                   race_final_labelMore than one race pf_hp_a_scld
## 223                                race_final_labelOther pf_hp_a_scld
## 224                 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 225                                ethnicityNot Hispanic pf_hp_a_scld
## 226                        ethnicityUnknown/Not Reported pf_hp_a_scld
## 227                            ruralLiving in rural area pf_hp_a_scld
## 228                            ruralUnknown/Not Reported pf_hp_a_scld
## 229                             smokingSmoke or use vape pf_hp_a_scld
## 230                          smokingUnknown/Not Reported pf_hp_a_scld
## 231         sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 232                 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 233                 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 234 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 235 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 236         sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 237                    sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 238                                     sq_self_hep_bYes pf_hp_a_scld
## 239                    sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 240                                     sq_self_hep_cYes pf_hp_a_scld
## 241                supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 242                                 supp_meds_tylenolYes pf_hp_a_scld
## 243               supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 244                                supp_meds_steroidsYes pf_hp_a_scld
## 245                    sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 246                                     sq_water_wellYes pf_hp_a_scld
## 247          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 248                           sq_water_tap_unfilteredYes pf_hp_a_scld
## 249        sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 250                         sq_water_house_filtrationYes pf_hp_a_scld
## 251           sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 252                            sq_water_faucet_filterYes pf_hp_a_scld
## 253         sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 254                          sq_water_charcoal_filterYes pf_hp_a_scld
## 255                 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 256                                  sq_water_bottledYes pf_hp_a_scld
## 257                    sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 258                                     sq_water_noneYes pf_hp_a_scld
## 259              sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 260                               sq_water_other_typeYes pf_hp_a_scld
## 261                                           sourceDUKE    pfbs_scld
## 262                                           sourceNCSU    pfbs_scld
## 263                                            sourceUNC    pfbs_scld
## 264                                              sexMale    pfbs_scld
## 265                                    race_eth_labelNHB    pfbs_scld
## 266                                    race_eth_labelNHO    pfbs_scld
## 267                                    race_eth_labelNHW    pfbs_scld
## 268                   race_eth_labelUnknown/Not Reported    pfbs_scld
## 269                      race_final_labelAmerican Indian    pfbs_scld
## 270       race_final_labelAmerican Indian/Alaskan Native    pfbs_scld
## 271                                race_final_labelAsian    pfbs_scld
## 272               race_final_labelAsian/Pacific Islander    pfbs_scld
## 273                                race_final_labelBlack    pfbs_scld
## 274                   race_final_labelMore than one race    pfbs_scld
## 275                                race_final_labelOther    pfbs_scld
## 276                 race_final_labelUnknown/Not Reported    pfbs_scld
## 277                                ethnicityNot Hispanic    pfbs_scld
## 278                        ethnicityUnknown/Not Reported    pfbs_scld
## 279                            ruralLiving in rural area    pfbs_scld
## 280                            ruralUnknown/Not Reported    pfbs_scld
## 281                             smokingSmoke or use vape    pfbs_scld
## 282                          smokingUnknown/Not Reported    pfbs_scld
## 283         sq_drink_alcoholNo, former drinker (stopped)    pfbs_scld
## 284                 sq_drink_alcoholUnknown/Not Reported    pfbs_scld
## 285                 sq_drink_alcoholYes, current drinker    pfbs_scld
## 286 sq_average_drink_per_day1-2 alcoholic drinks per day    pfbs_scld
## 287 sq_average_drink_per_day3-4 alcoholic drinks per day    pfbs_scld
## 288         sq_average_drink_per_dayUnknown/Not Reported    pfbs_scld
## 289                    sq_self_hep_bUnknown/Not Reported    pfbs_scld
## 290                                     sq_self_hep_bYes    pfbs_scld
## 291                    sq_self_hep_cUnknown/Not Reported    pfbs_scld
## 292                                     sq_self_hep_cYes    pfbs_scld
## 293                supp_meds_tylenolUnknown/Not Reported    pfbs_scld
## 294                                 supp_meds_tylenolYes    pfbs_scld
## 295               supp_meds_steroidsUnknown/Not Reported    pfbs_scld
## 296                                supp_meds_steroidsYes    pfbs_scld
## 297                    sq_water_wellUnknown/Not Reported    pfbs_scld
## 298                                     sq_water_wellYes    pfbs_scld
## 299          sq_water_tap_unfilteredUnknown/Not Reported    pfbs_scld
## 300                           sq_water_tap_unfilteredYes    pfbs_scld
## 301        sq_water_house_filtrationUnknown/Not Reported    pfbs_scld
## 302                         sq_water_house_filtrationYes    pfbs_scld
## 303           sq_water_faucet_filterUnknown/Not Reported    pfbs_scld
## 304                            sq_water_faucet_filterYes    pfbs_scld
## 305         sq_water_charcoal_filterUnknown/Not Reported    pfbs_scld
## 306                          sq_water_charcoal_filterYes    pfbs_scld
## 307                 sq_water_bottledUnknown/Not Reported    pfbs_scld
## 308                                  sq_water_bottledYes    pfbs_scld
## 309                    sq_water_noneUnknown/Not Reported    pfbs_scld
## 310                                     sq_water_noneYes    pfbs_scld
## 311              sq_water_other_typeUnknown/Not Reported    pfbs_scld
## 312                               sq_water_other_typeYes    pfbs_scld
## 313                                           sourceDUKE    pfoa_scld
## 314                                           sourceNCSU    pfoa_scld
## 315                                            sourceUNC    pfoa_scld
## 316                                              sexMale    pfoa_scld
## 317                                    race_eth_labelNHB    pfoa_scld
## 318                                    race_eth_labelNHO    pfoa_scld
## 319                                    race_eth_labelNHW    pfoa_scld
## 320                   race_eth_labelUnknown/Not Reported    pfoa_scld
## 321                      race_final_labelAmerican Indian    pfoa_scld
## 322       race_final_labelAmerican Indian/Alaskan Native    pfoa_scld
## 323                                race_final_labelAsian    pfoa_scld
## 324               race_final_labelAsian/Pacific Islander    pfoa_scld
## 325                                race_final_labelBlack    pfoa_scld
## 326                   race_final_labelMore than one race    pfoa_scld
## 327                                race_final_labelOther    pfoa_scld
## 328                 race_final_labelUnknown/Not Reported    pfoa_scld
## 329                                ethnicityNot Hispanic    pfoa_scld
## 330                        ethnicityUnknown/Not Reported    pfoa_scld
## 331                            ruralLiving in rural area    pfoa_scld
## 332                            ruralUnknown/Not Reported    pfoa_scld
## 333                             smokingSmoke or use vape    pfoa_scld
## 334                          smokingUnknown/Not Reported    pfoa_scld
## 335         sq_drink_alcoholNo, former drinker (stopped)    pfoa_scld
## 336                 sq_drink_alcoholUnknown/Not Reported    pfoa_scld
## 337                 sq_drink_alcoholYes, current drinker    pfoa_scld
## 338 sq_average_drink_per_day1-2 alcoholic drinks per day    pfoa_scld
## 339 sq_average_drink_per_day3-4 alcoholic drinks per day    pfoa_scld
## 340         sq_average_drink_per_dayUnknown/Not Reported    pfoa_scld
## 341                    sq_self_hep_bUnknown/Not Reported    pfoa_scld
## 342                                     sq_self_hep_bYes    pfoa_scld
## 343                    sq_self_hep_cUnknown/Not Reported    pfoa_scld
## 344                                     sq_self_hep_cYes    pfoa_scld
## 345                supp_meds_tylenolUnknown/Not Reported    pfoa_scld
## 346                                 supp_meds_tylenolYes    pfoa_scld
## 347               supp_meds_steroidsUnknown/Not Reported    pfoa_scld
## 348                                supp_meds_steroidsYes    pfoa_scld
## 349                    sq_water_wellUnknown/Not Reported    pfoa_scld
## 350                                     sq_water_wellYes    pfoa_scld
## 351          sq_water_tap_unfilteredUnknown/Not Reported    pfoa_scld
## 352                           sq_water_tap_unfilteredYes    pfoa_scld
## 353        sq_water_house_filtrationUnknown/Not Reported    pfoa_scld
## 354                         sq_water_house_filtrationYes    pfoa_scld
## 355           sq_water_faucet_filterUnknown/Not Reported    pfoa_scld
## 356                            sq_water_faucet_filterYes    pfoa_scld
## 357         sq_water_charcoal_filterUnknown/Not Reported    pfoa_scld
## 358                          sq_water_charcoal_filterYes    pfoa_scld
## 359                 sq_water_bottledUnknown/Not Reported    pfoa_scld
## 360                                  sq_water_bottledYes    pfoa_scld
## 361                    sq_water_noneUnknown/Not Reported    pfoa_scld
## 362                                     sq_water_noneYes    pfoa_scld
## 363              sq_water_other_typeUnknown/Not Reported    pfoa_scld
## 364                               sq_water_other_typeYes    pfoa_scld
## 365                                           sourceDUKE pf_pe_a_scld
## 366                                           sourceNCSU pf_pe_a_scld
## 367                                            sourceUNC pf_pe_a_scld
## 368                                              sexMale pf_pe_a_scld
## 369                                    race_eth_labelNHB pf_pe_a_scld
## 370                                    race_eth_labelNHO pf_pe_a_scld
## 371                                    race_eth_labelNHW pf_pe_a_scld
## 372                   race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 373                      race_final_labelAmerican Indian pf_pe_a_scld
## 374       race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 375                                race_final_labelAsian pf_pe_a_scld
## 376               race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 377                                race_final_labelBlack pf_pe_a_scld
## 378                   race_final_labelMore than one race pf_pe_a_scld
## 379                                race_final_labelOther pf_pe_a_scld
## 380                 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 381                                ethnicityNot Hispanic pf_pe_a_scld
## 382                        ethnicityUnknown/Not Reported pf_pe_a_scld
## 383                            ruralLiving in rural area pf_pe_a_scld
## 384                            ruralUnknown/Not Reported pf_pe_a_scld
## 385                             smokingSmoke or use vape pf_pe_a_scld
## 386                          smokingUnknown/Not Reported pf_pe_a_scld
## 387         sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 388                 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 389                 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 390 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 391 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 392         sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 393                    sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 394                                     sq_self_hep_bYes pf_pe_a_scld
## 395                    sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 396                                     sq_self_hep_cYes pf_pe_a_scld
## 397                supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 398                                 supp_meds_tylenolYes pf_pe_a_scld
## 399               supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 400                                supp_meds_steroidsYes pf_pe_a_scld
## 401                    sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 402                                     sq_water_wellYes pf_pe_a_scld
## 403          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 404                           sq_water_tap_unfilteredYes pf_pe_a_scld
## 405        sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 406                         sq_water_house_filtrationYes pf_pe_a_scld
## 407           sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 408                            sq_water_faucet_filterYes pf_pe_a_scld
## 409         sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 410                          sq_water_charcoal_filterYes pf_pe_a_scld
## 411                 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 412                                  sq_water_bottledYes pf_pe_a_scld
## 413                    sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 414                                     sq_water_noneYes pf_pe_a_scld
## 415              sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 416                               sq_water_other_typeYes pf_pe_a_scld
## 417                                           sourceDUKE pf_un_a_scld
## 418                                           sourceNCSU pf_un_a_scld
## 419                                            sourceUNC pf_un_a_scld
## 420                                              sexMale pf_un_a_scld
## 421                                    race_eth_labelNHB pf_un_a_scld
## 422                                    race_eth_labelNHO pf_un_a_scld
## 423                                    race_eth_labelNHW pf_un_a_scld
## 424                   race_eth_labelUnknown/Not Reported pf_un_a_scld
## 425                      race_final_labelAmerican Indian pf_un_a_scld
## 426       race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 427                                race_final_labelAsian pf_un_a_scld
## 428               race_final_labelAsian/Pacific Islander pf_un_a_scld
## 429                                race_final_labelBlack pf_un_a_scld
## 430                   race_final_labelMore than one race pf_un_a_scld
## 431                                race_final_labelOther pf_un_a_scld
## 432                 race_final_labelUnknown/Not Reported pf_un_a_scld
## 433                                ethnicityNot Hispanic pf_un_a_scld
## 434                        ethnicityUnknown/Not Reported pf_un_a_scld
## 435                            ruralLiving in rural area pf_un_a_scld
## 436                            ruralUnknown/Not Reported pf_un_a_scld
## 437                             smokingSmoke or use vape pf_un_a_scld
## 438                          smokingUnknown/Not Reported pf_un_a_scld
## 439         sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 440                 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 441                 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 442 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 443 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 444         sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 445                    sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 446                                     sq_self_hep_bYes pf_un_a_scld
## 447                    sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 448                                     sq_self_hep_cYes pf_un_a_scld
## 449                supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 450                                 supp_meds_tylenolYes pf_un_a_scld
## 451               supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 452                                supp_meds_steroidsYes pf_un_a_scld
## 453                    sq_water_wellUnknown/Not Reported pf_un_a_scld
## 454                                     sq_water_wellYes pf_un_a_scld
## 455          sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 456                           sq_water_tap_unfilteredYes pf_un_a_scld
## 457        sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 458                         sq_water_house_filtrationYes pf_un_a_scld
## 459           sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 460                            sq_water_faucet_filterYes pf_un_a_scld
## 461         sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 462                          sq_water_charcoal_filterYes pf_un_a_scld
## 463                 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 464                                  sq_water_bottledYes pf_un_a_scld
## 465                    sq_water_noneUnknown/Not Reported pf_un_a_scld
## 466                                     sq_water_noneYes pf_un_a_scld
## 467              sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 468                               sq_water_other_typeYes pf_un_a_scld
## 469                                           sourceDUKE pf_hp_s_scld
## 470                                           sourceNCSU pf_hp_s_scld
## 471                                            sourceUNC pf_hp_s_scld
## 472                                              sexMale pf_hp_s_scld
## 473                                    race_eth_labelNHB pf_hp_s_scld
## 474                                    race_eth_labelNHO pf_hp_s_scld
## 475                                    race_eth_labelNHW pf_hp_s_scld
## 476                   race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 477                      race_final_labelAmerican Indian pf_hp_s_scld
## 478       race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 479                                race_final_labelAsian pf_hp_s_scld
## 480               race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 481                                race_final_labelBlack pf_hp_s_scld
## 482                   race_final_labelMore than one race pf_hp_s_scld
## 483                                race_final_labelOther pf_hp_s_scld
## 484                 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 485                                ethnicityNot Hispanic pf_hp_s_scld
## 486                        ethnicityUnknown/Not Reported pf_hp_s_scld
## 487                            ruralLiving in rural area pf_hp_s_scld
## 488                            ruralUnknown/Not Reported pf_hp_s_scld
## 489                             smokingSmoke or use vape pf_hp_s_scld
## 490                          smokingUnknown/Not Reported pf_hp_s_scld
## 491         sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 492                 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 493                 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 494 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 495 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 496         sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 497                    sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 498                                     sq_self_hep_bYes pf_hp_s_scld
## 499                    sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 500                                     sq_self_hep_cYes pf_hp_s_scld
## 501                supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 502                                 supp_meds_tylenolYes pf_hp_s_scld
## 503               supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 504                                supp_meds_steroidsYes pf_hp_s_scld
## 505                    sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 506                                     sq_water_wellYes pf_hp_s_scld
## 507          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 508                           sq_water_tap_unfilteredYes pf_hp_s_scld
## 509        sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 510                         sq_water_house_filtrationYes pf_hp_s_scld
## 511           sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 512                            sq_water_faucet_filterYes pf_hp_s_scld
## 513         sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 514                          sq_water_charcoal_filterYes pf_hp_s_scld
## 515                 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 516                                  sq_water_bottledYes pf_hp_s_scld
## 517                    sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 518                                     sq_water_noneYes pf_hp_s_scld
## 519              sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 520                               sq_water_other_typeYes pf_hp_s_scld
## 521                                           sourceDUKE pf_do_a_scld
## 522                                           sourceNCSU pf_do_a_scld
## 523                                            sourceUNC pf_do_a_scld
## 524                                              sexMale pf_do_a_scld
## 525                                    race_eth_labelNHB pf_do_a_scld
## 526                                    race_eth_labelNHO pf_do_a_scld
## 527                                    race_eth_labelNHW pf_do_a_scld
## 528                   race_eth_labelUnknown/Not Reported pf_do_a_scld
## 529                      race_final_labelAmerican Indian pf_do_a_scld
## 530       race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 531                                race_final_labelAsian pf_do_a_scld
## 532               race_final_labelAsian/Pacific Islander pf_do_a_scld
## 533                                race_final_labelBlack pf_do_a_scld
## 534                   race_final_labelMore than one race pf_do_a_scld
## 535                                race_final_labelOther pf_do_a_scld
## 536                 race_final_labelUnknown/Not Reported pf_do_a_scld
## 537                                ethnicityNot Hispanic pf_do_a_scld
## 538                        ethnicityUnknown/Not Reported pf_do_a_scld
## 539                            ruralLiving in rural area pf_do_a_scld
## 540                            ruralUnknown/Not Reported pf_do_a_scld
## 541                             smokingSmoke or use vape pf_do_a_scld
## 542                          smokingUnknown/Not Reported pf_do_a_scld
## 543         sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 544                 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 545                 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 546 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 547 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 548         sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 549                    sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 550                                     sq_self_hep_bYes pf_do_a_scld
## 551                    sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 552                                     sq_self_hep_cYes pf_do_a_scld
## 553                supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 554                                 supp_meds_tylenolYes pf_do_a_scld
## 555               supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 556                                supp_meds_steroidsYes pf_do_a_scld
## 557                    sq_water_wellUnknown/Not Reported pf_do_a_scld
## 558                                     sq_water_wellYes pf_do_a_scld
## 559          sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 560                           sq_water_tap_unfilteredYes pf_do_a_scld
## 561        sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 562                         sq_water_house_filtrationYes pf_do_a_scld
## 563           sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 564                            sq_water_faucet_filterYes pf_do_a_scld
## 565         sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 566                          sq_water_charcoal_filterYes pf_do_a_scld
## 567                 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 568                                  sq_water_bottledYes pf_do_a_scld
## 569                    sq_water_noneUnknown/Not Reported pf_do_a_scld
## 570                                     sq_water_noneYes pf_do_a_scld
## 571              sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 572                               sq_water_other_typeYes pf_do_a_scld
## 573                                           sourceDUKE pf_pe_s_scld
## 574                                           sourceNCSU pf_pe_s_scld
## 575                                            sourceUNC pf_pe_s_scld
## 576                                              sexMale pf_pe_s_scld
## 577                                    race_eth_labelNHB pf_pe_s_scld
## 578                                    race_eth_labelNHO pf_pe_s_scld
## 579                                    race_eth_labelNHW pf_pe_s_scld
## 580                   race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 581                      race_final_labelAmerican Indian pf_pe_s_scld
## 582       race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 583                                race_final_labelAsian pf_pe_s_scld
## 584               race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 585                                race_final_labelBlack pf_pe_s_scld
## 586                   race_final_labelMore than one race pf_pe_s_scld
## 587                                race_final_labelOther pf_pe_s_scld
## 588                 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 589                                ethnicityNot Hispanic pf_pe_s_scld
## 590                        ethnicityUnknown/Not Reported pf_pe_s_scld
## 591                            ruralLiving in rural area pf_pe_s_scld
## 592                            ruralUnknown/Not Reported pf_pe_s_scld
## 593                             smokingSmoke or use vape pf_pe_s_scld
## 594                          smokingUnknown/Not Reported pf_pe_s_scld
## 595         sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 596                 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 597                 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 598 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 599 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 600         sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 601                    sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 602                                     sq_self_hep_bYes pf_pe_s_scld
## 603                    sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 604                                     sq_self_hep_cYes pf_pe_s_scld
## 605                supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 606                                 supp_meds_tylenolYes pf_pe_s_scld
## 607               supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 608                                supp_meds_steroidsYes pf_pe_s_scld
## 609                    sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 610                                     sq_water_wellYes pf_pe_s_scld
## 611          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 612                           sq_water_tap_unfilteredYes pf_pe_s_scld
## 613        sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 614                         sq_water_house_filtrationYes pf_pe_s_scld
## 615           sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 616                            sq_water_faucet_filterYes pf_pe_s_scld
## 617         sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 618                          sq_water_charcoal_filterYes pf_pe_s_scld
## 619                 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 620                                  sq_water_bottledYes pf_pe_s_scld
## 621                    sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 622                                     sq_water_noneYes pf_pe_s_scld
## 623              sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 624                               sq_water_other_typeYes pf_pe_s_scld
## 625                                           sourceDUKE pf_hx_a_scld
## 626                                           sourceNCSU pf_hx_a_scld
## 627                                            sourceUNC pf_hx_a_scld
## 628                                              sexMale pf_hx_a_scld
## 629                                    race_eth_labelNHB pf_hx_a_scld
## 630                                    race_eth_labelNHO pf_hx_a_scld
## 631                                    race_eth_labelNHW pf_hx_a_scld
## 632                   race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 633                      race_final_labelAmerican Indian pf_hx_a_scld
## 634       race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 635                                race_final_labelAsian pf_hx_a_scld
## 636               race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 637                                race_final_labelBlack pf_hx_a_scld
## 638                   race_final_labelMore than one race pf_hx_a_scld
## 639                                race_final_labelOther pf_hx_a_scld
## 640                 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 641                                ethnicityNot Hispanic pf_hx_a_scld
## 642                        ethnicityUnknown/Not Reported pf_hx_a_scld
## 643                            ruralLiving in rural area pf_hx_a_scld
## 644                            ruralUnknown/Not Reported pf_hx_a_scld
## 645                             smokingSmoke or use vape pf_hx_a_scld
## 646                          smokingUnknown/Not Reported pf_hx_a_scld
## 647         sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 648                 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 649                 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 650 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 651 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 652         sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 653                    sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 654                                     sq_self_hep_bYes pf_hx_a_scld
## 655                    sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 656                                     sq_self_hep_cYes pf_hx_a_scld
## 657                supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 658                                 supp_meds_tylenolYes pf_hx_a_scld
## 659               supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 660                                supp_meds_steroidsYes pf_hx_a_scld
## 661                    sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 662                                     sq_water_wellYes pf_hx_a_scld
## 663          sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 664                           sq_water_tap_unfilteredYes pf_hx_a_scld
## 665        sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 666                         sq_water_house_filtrationYes pf_hx_a_scld
## 667           sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 668                            sq_water_faucet_filterYes pf_hx_a_scld
## 669         sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 670                          sq_water_charcoal_filterYes pf_hx_a_scld
## 671                 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 672                                  sq_water_bottledYes pf_hx_a_scld
## 673                    sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 674                                     sq_water_noneYes pf_hx_a_scld
## 675              sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 676                               sq_water_other_typeYes pf_hx_a_scld
## 677                                           sourceDUKE    pfba_scld
## 678                                           sourceNCSU    pfba_scld
## 679                                            sourceUNC    pfba_scld
## 680                                              sexMale    pfba_scld
## 681                                    race_eth_labelNHB    pfba_scld
## 682                                    race_eth_labelNHO    pfba_scld
## 683                                    race_eth_labelNHW    pfba_scld
## 684                   race_eth_labelUnknown/Not Reported    pfba_scld
## 685                      race_final_labelAmerican Indian    pfba_scld
## 686       race_final_labelAmerican Indian/Alaskan Native    pfba_scld
## 687                                race_final_labelAsian    pfba_scld
## 688               race_final_labelAsian/Pacific Islander    pfba_scld
## 689                                race_final_labelBlack    pfba_scld
## 690                   race_final_labelMore than one race    pfba_scld
## 691                                race_final_labelOther    pfba_scld
## 692                 race_final_labelUnknown/Not Reported    pfba_scld
## 693                                ethnicityNot Hispanic    pfba_scld
## 694                        ethnicityUnknown/Not Reported    pfba_scld
## 695                            ruralLiving in rural area    pfba_scld
## 696                            ruralUnknown/Not Reported    pfba_scld
## 697                             smokingSmoke or use vape    pfba_scld
## 698                          smokingUnknown/Not Reported    pfba_scld
## 699         sq_drink_alcoholNo, former drinker (stopped)    pfba_scld
## 700                 sq_drink_alcoholUnknown/Not Reported    pfba_scld
## 701                 sq_drink_alcoholYes, current drinker    pfba_scld
## 702 sq_average_drink_per_day1-2 alcoholic drinks per day    pfba_scld
## 703 sq_average_drink_per_day3-4 alcoholic drinks per day    pfba_scld
## 704         sq_average_drink_per_dayUnknown/Not Reported    pfba_scld
## 705                    sq_self_hep_bUnknown/Not Reported    pfba_scld
## 706                                     sq_self_hep_bYes    pfba_scld
## 707                    sq_self_hep_cUnknown/Not Reported    pfba_scld
## 708                                     sq_self_hep_cYes    pfba_scld
## 709                supp_meds_tylenolUnknown/Not Reported    pfba_scld
## 710                                 supp_meds_tylenolYes    pfba_scld
## 711               supp_meds_steroidsUnknown/Not Reported    pfba_scld
## 712                                supp_meds_steroidsYes    pfba_scld
## 713                    sq_water_wellUnknown/Not Reported    pfba_scld
## 714                                     sq_water_wellYes    pfba_scld
## 715          sq_water_tap_unfilteredUnknown/Not Reported    pfba_scld
## 716                           sq_water_tap_unfilteredYes    pfba_scld
## 717        sq_water_house_filtrationUnknown/Not Reported    pfba_scld
## 718                         sq_water_house_filtrationYes    pfba_scld
## 719           sq_water_faucet_filterUnknown/Not Reported    pfba_scld
## 720                            sq_water_faucet_filterYes    pfba_scld
## 721         sq_water_charcoal_filterUnknown/Not Reported    pfba_scld
## 722                          sq_water_charcoal_filterYes    pfba_scld
## 723                 sq_water_bottledUnknown/Not Reported    pfba_scld
## 724                                  sq_water_bottledYes    pfba_scld
## 725                    sq_water_noneUnknown/Not Reported    pfba_scld
## 726                                     sq_water_noneYes    pfba_scld
## 727              sq_water_other_typeUnknown/Not Reported    pfba_scld
## 728                               sq_water_other_typeYes    pfba_scld
# Combine and print all results
all_results <- rbind(continuous_results, categorical_results)
print(all_results)
##                   Confounders          Coeff                   P
## 1           age_at_enrollment  0.02303963456 0.00002886969906048
## 2                         bmi -0.01294812141 0.09611802745084295
## 3                 trig_mg_d_l  0.00006026119 0.92894071463283046
## 4           age_at_enrollment  0.01401689114 0.01151280974826796
## 5                         bmi -0.01953451965 0.01049324625441954
## 6                 trig_mg_d_l -0.00087134772 0.19560135743360868
## 7           age_at_enrollment  0.02285242310 0.00003418692654294
## 8                         bmi -0.01016268100 0.19162630103330469
## 9                 trig_mg_d_l -0.00012960498 0.84543151201780353
## 10          age_at_enrollment  0.03015972790 0.00000003849922364
## 11                        bmi -0.00632410823 0.39797483121906918
## 12                trig_mg_d_l -0.00060423845 0.36564464944825859
## 13          age_at_enrollment  0.00610856935 0.27012846280853531
## 14                        bmi -0.00353210464 0.63621285405847949
## 15                trig_mg_d_l -0.00037474559 0.57810255513241993
## 16          age_at_enrollment -0.00039411970 0.94418956366096030
## 17                        bmi  0.00294487490 0.67943161804413910
## 18                trig_mg_d_l -0.00051069442 0.45289086346786955
## 19          age_at_enrollment  0.01990379953 0.00032706545799936
## 20                        bmi -0.01850220830 0.01730193122393180
## 21                trig_mg_d_l  0.00025406806 0.70465471480120978
## 22          age_at_enrollment  0.00422728729 0.41715088028540981
## 23                        bmi -0.00405085209 0.60753830998090086
## 24                trig_mg_d_l -0.00053365608 0.39127771559889879
## 25          age_at_enrollment  0.01474075004 0.00829630670610244
## 26                        bmi -0.02788390573 0.00016089041529066
## 27                trig_mg_d_l -0.00105386273 0.11863558257777872
## 28          age_at_enrollment  0.03647003290 0.00000000001254972
## 29                        bmi -0.00498536122 0.49783998070122160
## 30                trig_mg_d_l  0.00035106604 0.59487932766741591
## 31          age_at_enrollment  0.00666942172 0.23478386028671305
## 32                        bmi -0.02691555479 0.00053014713321087
## 33                trig_mg_d_l -0.00088518107 0.19110820055049224
## 34          age_at_enrollment  0.00723125430 0.18396989965273475
## 35                        bmi -0.02551872568 0.00081610677927414
## 36                trig_mg_d_l -0.00011850725 0.86073672780481414
## 37          age_at_enrollment -0.00767217344 0.16771220778328327
## 38                        bmi  0.00258183929 0.73804930867047069
## 39                trig_mg_d_l -0.00082944038 0.21370329383697809
## 40          age_at_enrollment  0.00156141004 0.75969057121585326
## 41                        bmi -0.00261010632 0.73748118274195495
## 42                trig_mg_d_l -0.00015137036 0.80919075133267548
## 43                     source  0.20089579824 0.17816637785405615
## 44                     source  0.28773100400 0.02406241741592427
## 45                     source -0.24039891626 0.34812012829126060
## 46                        sex  0.18719769132 0.08219582251875361
## 47             race_eth_label  0.05078849200 0.84233629638900953
## 48             race_eth_label -0.11510427163 0.74149107535181924
## 49             race_eth_label  0.27422981845 0.26292213105598811
## 50             race_eth_label  0.03888580877 0.89888254812528012
## 51           race_final_label -0.12541197098 0.86037830965615614
## 52           race_final_label -0.56765432777 0.26261635127196636
## 53           race_final_label -0.09411026424 0.87184652297268461
## 54           race_final_label -0.40797332376 0.32604479871633008
## 55           race_final_label -0.20988620230 0.07137795642001048
## 56           race_final_label -0.26436616917 0.79275654669035089
## 57           race_final_label -0.29339427221 0.32387441802348305
## 58           race_final_label -0.14073047032 0.71483775593414878
## 59                  ethnicity  0.18917716778 0.43536968718843172
## 60                  ethnicity  0.03888580877 0.89927233218110325
## 61                      rural -0.24655106670 0.18530426138296391
## 62                      rural -0.14787709652 0.36453370981273614
## 63                    smoking -0.32381027411 0.07305486987872799
## 64                    smoking -0.22948580632 0.04908826819920185
## 65           sq_drink_alcohol -0.09194220808 0.55776878080576808
## 66           sq_drink_alcohol -0.22484955476 0.12238274703819219
## 67           sq_drink_alcohol -0.00556695961 0.96937817849957508
## 68   sq_average_drink_per_day  0.11608471601 0.65578352846413657
## 69   sq_average_drink_per_day -0.26182699346 0.53666952886876229
## 70   sq_average_drink_per_day -0.10529965991 0.40074255201137199
## 71              sq_self_hep_b -0.14193861153 0.21157942812386105
## 72              sq_self_hep_b  0.19215207005 0.44361557003895957
## 73              sq_self_hep_c -0.14660686900 0.19870154554602260
## 74              sq_self_hep_c -0.15523926134 0.52527191567200049
## 75          supp_meds_tylenol  0.30494095510 0.42547659640687141
## 76          supp_meds_tylenol  0.41908332375 0.50488516530129868
## 77         supp_meds_steroids  0.04092410029 0.90371151384786230
## 78         supp_meds_steroids -0.65705384429 0.53425913283768089
## 79              sq_water_well -0.20912105580 0.06225382264052396
## 80              sq_water_well -0.21812448525 0.15388307408751842
## 81    sq_water_tap_unfiltered -0.02362122330 0.86549139364514827
## 82    sq_water_tap_unfiltered  0.24293059625 0.06779584280841820
## 83  sq_water_house_filtration -0.16476832282 0.13133740209378553
## 84  sq_water_house_filtration -0.01594137263 0.93081104646967838
## 85     sq_water_faucet_filter -0.13799983326 0.25477840057299572
## 86     sq_water_faucet_filter  0.05896978212 0.65148799314023809
## 87   sq_water_charcoal_filter -0.15783530672 0.15697464465805316
## 88   sq_water_charcoal_filter  0.07812098769 0.61742210595572367
## 89           sq_water_bottled -0.48924546335 0.00049146516070939
## 90           sq_water_bottled -0.42565285199 0.00165296201584316
## 91              sq_water_none -0.15570725838 0.14497821238538158
## 92              sq_water_none -0.40194594145 0.10160522807343024
## 93        sq_water_other_type -0.14423859690 0.18408036183404880
## 94        sq_water_other_type -0.17196169235 0.40129154196832417
## 95                     source -0.24632049084 0.09903735645483616
## 96                     source -0.08968999822 0.48062363680237086
## 97                     source -0.66412389592 0.00983867718366343
## 98                        sex -0.13271702075 0.21831847824619438
## 99             race_eth_label  0.12471352645 0.62644193500230960
## 100            race_eth_label  0.42784565581 0.22209485749044836
## 101            race_eth_label  0.21907404512 0.37257706140278501
## 102            race_eth_label -0.05139885416 0.86705038235471044
## 103          race_final_label -0.12904195608 0.85548381268167228
## 104          race_final_label -0.48022761545 0.34013223492179612
## 105          race_final_label  0.22162573034 0.70227327313975429
## 106          race_final_label  0.73131111208 0.07689498379754887
## 107          race_final_label -0.10601570408 0.35862071309513155
## 108          race_final_label  1.83044970969 0.06776455127016796
## 109          race_final_label -0.18187519687 0.53812055995120500
## 110          race_final_label -0.42628443262 0.26576060685671926
## 111                 ethnicity  0.19991584684 0.40915967427517430
## 112                 ethnicity -0.05139885416 0.86694944980857691
## 113                     rural -0.22321300609 0.23079719614026042
## 114                     rural -0.08991929292 0.58161444509189819
## 115                   smoking -0.47778649069 0.00796269280740328
## 116                   smoking -0.26851843856 0.02067734371999552
## 117          sq_drink_alcohol -0.20787027884 0.17690069057809690
## 118          sq_drink_alcohol -0.13357448819 0.34875449247860013
## 119          sq_drink_alcohol  0.34307142692 0.01623256250436837
## 120  sq_average_drink_per_day -0.06581956258 0.79640617614150799
## 121  sq_average_drink_per_day  0.30971082539 0.45570924404054869
## 122  sq_average_drink_per_day -0.44982168589 0.00027963169345085
## 123             sq_self_hep_b -0.19517872802 0.08568444046719309
## 124             sq_self_hep_b  0.12479265729 0.61823627484439159
## 125             sq_self_hep_c -0.18857557102 0.09718837256862224
## 126             sq_self_hep_c  0.29382990208 0.22767940372032977
## 127         supp_meds_tylenol -0.10442649184 0.78494873278749755
## 128         supp_meds_tylenol  0.13718883533 0.82724883059649901
## 129        supp_meds_steroids -0.20962233873 0.53564564076426890
## 130        supp_meds_steroids -0.22238305274 0.83337724859734486
## 131             sq_water_well -0.13268719802 0.23787633145927936
## 132             sq_water_well -0.09790215802 0.52323933577829118
## 133   sq_water_tap_unfiltered -0.07182516640 0.60904913514095971
## 134   sq_water_tap_unfiltered  0.04709023866 0.72458169476826395
## 135 sq_water_house_filtration -0.11848550442 0.27832139702085046
## 136 sq_water_house_filtration -0.06928087412 0.70639209383938706
## 137    sq_water_faucet_filter -0.11121502339 0.35955630258942806
## 138    sq_water_faucet_filter -0.00361724267 0.97793718366327420
## 139  sq_water_charcoal_filter -0.11828454491 0.28976269305198821
## 140  sq_water_charcoal_filter -0.03045970838 0.84593988600083936
## 141          sq_water_bottled -0.33888418838 0.01623333367882546
## 142          sq_water_bottled -0.32623957681 0.01651137591491339
## 143             sq_water_none -0.08153190201 0.44683501274426518
## 144             sq_water_none -0.16116851633 0.51271635885510181
## 145       sq_water_other_type -0.14035669239 0.19632346742509224
## 146       sq_water_other_type  0.00574184469 0.97764091282821886
## 147                    source -0.17536978665 0.24029175304654887
## 148                    source -0.00155751589 0.99023454642433784
## 149                    source -0.60420136676 0.01887733932902677
## 150                       sex -0.18249153129 0.09022350808632779
## 151            race_eth_label  0.11143909821 0.66399374875135109
## 152            race_eth_label  0.07538354085 0.82971361421867285
## 153            race_eth_label  0.23515848590 0.33914499829760592
## 154            race_eth_label  0.01020295708 0.97352278690986982
## 155          race_final_label -0.03100524184 0.96535916817740675
## 156          race_final_label -0.43936740430 0.38636569597660375
## 157          race_final_label -0.01042341464 0.98576358357417404
## 158          race_final_label -0.11707470105 0.77822396547545969
## 159          race_final_label -0.11828239505 0.30947290327306004
## 160          race_final_label  1.41533588799 0.16063322156113585
## 161          race_final_label -0.23230174880 0.43522678347209232
## 162          race_final_label -0.25018833673 0.51661750631772252
## 163                 ethnicity  0.19057081425 0.43185377958703053
## 164                 ethnicity  0.01020295708 0.97349654239087935
## 165                     rural -0.16657258549 0.37123860525779162
## 166                     rural -0.12227732775 0.45397511461727769
## 167                   smoking -0.55049716636 0.00222769302932113
## 168                   smoking -0.24303361665 0.03579987987392344
## 169          sq_drink_alcohol -0.15214353078 0.32289373258876941
## 170          sq_drink_alcohol -0.06457873589 0.65051917779037560
## 171          sq_drink_alcohol  0.39510198704 0.00571563030051612
## 172  sq_average_drink_per_day -0.17936334767 0.48139369719176539
## 173  sq_average_drink_per_day  0.33975607179 0.41243345212405735
## 174  sq_average_drink_per_day -0.47566543041 0.00012072205421371
## 175             sq_self_hep_b -0.16998081239 0.13479409364303724
## 176             sq_self_hep_b  0.07765272744 0.75677422534221872
## 177             sq_self_hep_c -0.15324170559 0.17920855998162405
## 178             sq_self_hep_c -0.04152663629 0.86504317800143804
## 179         supp_meds_tylenol  0.13783174590 0.71761513785548414
## 180         supp_meds_tylenol  1.04553009228 0.09550981098712773
## 181        supp_meds_steroids -0.30899292899 0.36097388049451384
## 182        supp_meds_steroids  0.10145385531 0.92347692411988780
## 183             sq_water_well -0.19791095634 0.07794860503604970
## 184             sq_water_well -0.14505144638 0.34325528210841694
## 185   sq_water_tap_unfiltered -0.06685848275 0.63379349809383023
## 186   sq_water_tap_unfiltered  0.07774682125 0.56054814450977120
## 187 sq_water_house_filtration -0.15719663895 0.15011332667990274
## 188 sq_water_house_filtration -0.06359736630 0.72917960795943682
## 189    sq_water_faucet_filter -0.16765748230 0.16701503357658093
## 190    sq_water_faucet_filter -0.06287281614 0.63038370929637866
## 191  sq_water_charcoal_filter -0.17148290513 0.12453939373143023
## 192  sq_water_charcoal_filter -0.08827373415 0.57281724091754371
## 193          sq_water_bottled -0.31695071374 0.02481255320482565
## 194          sq_water_bottled -0.25741602293 0.05871655083207015
## 195             sq_water_none -0.15039524262 0.15986807660112737
## 196             sq_water_none -0.28059297095 0.25347435646347488
## 197       sq_water_other_type -0.17879316843 0.09958577804326338
## 198       sq_water_other_type -0.06326299693 0.75719660380250520
## 199                    source -0.02812398302 0.85071911056610372
## 200                    source  0.06545950319 0.60757504689611586
## 201                    source -0.54934387889 0.03293039529831022
## 202                       sex  0.17018018712 0.11421477651813232
## 203            race_eth_label  0.17735142739 0.48947961492581005
## 204            race_eth_label  0.21368881066 0.54222117666899239
## 205            race_eth_label  0.22072926125 0.36962368798292344
## 206            race_eth_label -0.06425467746 0.83445298522020916
## 207          race_final_label -0.25650160083 0.71719909410729121
## 208          race_final_label -0.61212402402 0.22393436309559747
## 209          race_final_label  0.30221826119 0.60200810793987869
## 210          race_final_label  0.34506062946 0.40285038365018688
## 211          race_final_label -0.03033696946 0.79256603065346942
## 212          race_final_label  2.50633119704 0.01250591825764212
## 213          race_final_label -0.26150115907 0.37590551468124966
## 214          race_final_label -0.35693228717 0.35102751784317132
## 215                 ethnicity  0.20729582419 0.39191644876826492
## 216                 ethnicity -0.06425467746 0.83404410390462613
## 217                     rural -0.15050379014 0.41945767850033333
## 218                     rural -0.02567749874 0.87510243365735252
## 219                   smoking -0.56816147223 0.00162483462451516
## 220                   smoking -0.17764283744 0.12484388703392600
## 221          sq_drink_alcohol -0.09279790313 0.55221901412799523
## 222          sq_drink_alcohol -0.03617257237 0.80250707464714910
## 223          sq_drink_alcohol  0.25098982175 0.08264764890083667
## 224  sq_average_drink_per_day -0.14733130408 0.56878498930871513
## 225  sq_average_drink_per_day  0.13741276657 0.74388410618383627
## 226  sq_average_drink_per_day -0.30905433609 0.01330787645466807
## 227             sq_self_hep_b -0.09608172406 0.39765419685309278
## 228             sq_self_hep_b  0.27321564105 0.27638203097614938
## 229             sq_self_hep_c -0.08827848999 0.43852931773111470
## 230             sq_self_hep_c  0.26631945107 0.27592232776750170
## 231         supp_meds_tylenol  0.22225409868 0.56129273508638611
## 232         supp_meds_tylenol  0.49018745604 0.43550301353660792
## 233        supp_meds_steroids -0.00137046605 0.99676971448586982
## 234        supp_meds_steroids -0.08300121298 0.93744563737665432
## 235             sq_water_well -0.13821435834 0.21869101547986863
## 236             sq_water_well -0.14605474755 0.34083898263584533
## 237   sq_water_tap_unfiltered -0.10884128949 0.43862900238366409
## 238   sq_water_tap_unfiltered -0.03152028087 0.81362118915627635
## 239 sq_water_house_filtration -0.10144195077 0.35332742478950041
## 240 sq_water_house_filtration  0.02881244426 0.87552600125445079
## 241    sq_water_faucet_filter -0.14451134890 0.23371706850434837
## 242    sq_water_faucet_filter -0.11576443844 0.37605746039474852
## 243  sq_water_charcoal_filter -0.09435771005 0.39811618261251014
## 244  sq_water_charcoal_filter  0.07837485262 0.61705027014982561
## 245          sq_water_bottled -0.37438959384 0.00776080774017603
## 246          sq_water_bottled -0.39800413798 0.00339692534565435
## 247             sq_water_none -0.09715229470 0.36444330562043414
## 248             sq_water_none -0.22134405610 0.36842362123646244
## 249       sq_water_other_type -0.11865182176 0.27482381292786012
## 250       sq_water_other_type -0.12858001415 0.53059508137585887
## 251                    source  0.10434243584 0.48142224323953187
## 252                    source -0.25317070261 0.04569477728082912
## 253                    source  0.36671855653 0.15030326977519787
## 254                       sex  0.04753972206 0.65952777755572933
## 255            race_eth_label -0.29217269122 0.24558725047749871
## 256            race_eth_label  0.25904064681 0.45098650537993790
## 257            race_eth_label  0.19739330324 0.41292192888074941
## 258            race_eth_label -0.00442983508 0.98826938151728350
## 259          race_final_label  2.99035334157 0.00001177078990472
## 260          race_final_label -0.75076319456 0.11711937574190467
## 261          race_final_label -0.21362555587 0.69833970735643969
## 262          race_final_label -0.20106093275 0.60825038534995124
## 263          race_final_label -0.52810627333 0.00000214067537662
## 264          race_final_label  0.14446620325 0.87920452393557480
## 265          race_final_label -0.54187633507 0.05422224390061022
## 266          race_final_label -0.64172021528 0.07841348875071134
## 267                 ethnicity  0.05211948502 0.82999524103981692
## 268                 ethnicity -0.00442983508 0.98850968884498225
## 269                     rural  0.03981842586 0.83089199323123042
## 270                     rural  0.06732367912 0.68047191960674125
## 271                   smoking -0.25860857221 0.15406242068696158
## 272                   smoking -0.04266261101 0.71521597801003944
## 273          sq_drink_alcohol  0.13586280492 0.38818440115655084
## 274          sq_drink_alcohol  0.04809961613 0.74153340009344393
## 275          sq_drink_alcohol  0.06560293585 0.65217578709575408
## 276  sq_average_drink_per_day  0.34358261099 0.18358567488178576
## 277  sq_average_drink_per_day  1.13305976415 0.00724023699402287
## 278  sq_average_drink_per_day  0.11291955180 0.36326518486072301
## 279             sq_self_hep_b -0.00524517482 0.96324302106208015
## 280             sq_self_hep_b -0.15705182864 0.53229219046349652
## 281             sq_self_hep_c  0.07666664632 0.50144498984545860
## 282             sq_self_hep_c  0.25616463159 0.29506967023341613
## 283         supp_meds_tylenol  0.14236624332 0.70995004114878091
## 284         supp_meds_tylenol  0.09558088307 0.87917492730682845
## 285        supp_meds_steroids  0.10616093523 0.75379520356562613
## 286        supp_meds_steroids -0.00628231156 0.99525978948954863
## 287             sq_water_well -0.07901703786 0.48242764822962969
## 288             sq_water_well -0.02984035320 0.84588876056705242
## 289   sq_water_tap_unfiltered  0.03916307547 0.78055819320376185
## 290   sq_water_tap_unfiltered  0.05959691935 0.65602092727267491
## 291 sq_water_house_filtration -0.06483291389 0.55318487403520389
## 292 sq_water_house_filtration  0.04468035213 0.80820617839498143
## 293    sq_water_faucet_filter  0.04534724923 0.70845488436320547
## 294    sq_water_faucet_filter  0.15306167211 0.24212063939652043
## 295  sq_water_charcoal_filter -0.08703304619 0.43594263519054810
## 296  sq_water_charcoal_filter -0.13294540628 0.39671140672906335
## 297          sq_water_bottled -0.16259904098 0.25016970657882531
## 298          sq_water_bottled -0.21830266548 0.10999177430940106
## 299             sq_water_none -0.05212974655 0.62687470346804519
## 300             sq_water_none  0.04128376170 0.86689577335487455
## 301       sq_water_other_type -0.08433147055 0.43780014503922282
## 302       sq_water_other_type  0.05985138573 0.77047252115801956
## 303                    source -0.24209283532 0.10550138874091017
## 304                    source -0.31476257326 0.01381394218896274
## 305                    source -0.43870902249 0.08785424567301257
## 306                       sex -0.07419888249 0.49160923227274667
## 307            race_eth_label -0.01464216199 0.95432754792565500
## 308            race_eth_label -0.02338660368 0.94662591167007082
## 309            race_eth_label  0.21620594043 0.37788131612548015
## 310            race_eth_label  0.29446925111 0.33681006421286097
## 311          race_final_label -0.06497780823 0.92729153707792866
## 312          race_final_label -0.36498230103 0.47055950006093461
## 313          race_final_label -0.28720334611 0.62210940950523153
## 314          race_final_label -0.23482160095 0.57120995522040818
## 315          race_final_label -0.26151753168 0.02465261727798186
## 316          race_final_label -0.32212450954 0.74855511160559651
## 317          race_final_label -0.27609206683 0.35256225658706697
## 318          race_final_label -0.33436959282 0.38492197181345200
## 319                 ethnicity  0.13563843592 0.57595190803645446
## 320                 ethnicity  0.29446925111 0.33814572280041677
## 321                     rural  0.40223577490 0.03046477151052450
## 322                     rural -0.02546298067 0.87544973896637868
## 323                   smoking  0.02876208111 0.87397920554472974
## 324                   smoking -0.13222001617 0.25890118865789558
## 325          sq_drink_alcohol  0.03093608928 0.84399176612941307
## 326          sq_drink_alcohol -0.11354395865 0.43588771007606608
## 327          sq_drink_alcohol  0.04248949045 0.77007949378540197
## 328  sq_average_drink_per_day  0.00173849948 0.99459668702331505
## 329  sq_average_drink_per_day  1.43403530887 0.00065732225553094
## 330  sq_average_drink_per_day  0.00140892991 0.99089640548374591
## 331             sq_self_hep_b -0.16164730427 0.15490547435844459
## 332             sq_self_hep_b -0.23109631067 0.35688641562230339
## 333             sq_self_hep_c -0.11062049213 0.33226578512771388
## 334             sq_self_hep_c  0.11736859792 0.63123268162752577
## 335         supp_meds_tylenol  0.05111422485 0.89376908860933635
## 336         supp_meds_tylenol -0.10000151347 0.87363945925546149
## 337        supp_meds_steroids  0.06764799759 0.84159227516948099
## 338        supp_meds_steroids -0.13545178545 0.89808056927624569
## 339             sq_water_well  0.02866859932 0.79878534040690352
## 340             sq_water_well -0.08368273867 0.58576338919294979
## 341   sq_water_tap_unfiltered -0.04878699378 0.72768354490860843
## 342   sq_water_tap_unfiltered -0.19626578122 0.14160342074544699
## 343 sq_water_house_filtration  0.06616698617 0.54472537125352871
## 344 sq_water_house_filtration  0.18374184953 0.31807308447128430
## 345    sq_water_faucet_filter  0.08488801129 0.48456664213754364
## 346    sq_water_faucet_filter  0.05917333707 0.65126163583327279
## 347  sq_water_charcoal_filter  0.06196009253 0.57934760076694691
## 348  sq_water_charcoal_filter  0.08690500377 0.57974234090501586
## 349          sq_water_bottled  0.00150677405 0.99150577538440487
## 350          sq_water_bottled -0.11319237198 0.40754821004133213
## 351             sq_water_none  0.03361244212 0.75393115719870929
## 352             sq_water_none -0.08365917336 0.73415922696710800
## 353       sq_water_other_type  0.02430377718 0.82315529229054696
## 354       sq_water_other_type -0.04733327578 0.81768277140597645
## 355                    source -0.07628426924 0.61053968426051308
## 356                    source  0.06932016021 0.58737751970846286
## 357                    source -0.42856769023 0.09643431102686301
## 358                       sex -0.12516480332 0.24571033496867928
## 359            race_eth_label -0.14944647943 0.55373366719946415
## 360            race_eth_label  0.13817704270 0.68861553399694753
## 361            race_eth_label  0.29883181637 0.21707705491636420
## 362            race_eth_label  0.01456195611 0.96159120427070899
## 363          race_final_label  0.23361134664 0.73943226114684735
## 364          race_final_label -0.68257158805 0.17165331000666206
## 365          race_final_label  0.06701195493 0.90716829723055903
## 366          race_final_label  0.01299508731 0.97464817278574101
## 367          race_final_label -0.43096670466 0.00019045927418025
## 368          race_final_label  0.54236038999 0.58429976766167235
## 369          race_final_label -0.30030274488 0.30534074418752660
## 370          race_final_label  0.02574794177 0.94588993724285442
## 371                 ethnicity  0.15605957242 0.51999177754676029
## 372                 ethnicity  0.01456195611 0.96220473628700909
## 373                     rural -0.19014848628 0.30702073717271200
## 374                     rural -0.17063001679 0.29578828788986516
## 375                   smoking -0.40978883374 0.02321464375482415
## 376                   smoking -0.22263200726 0.05574471466387071
## 377          sq_drink_alcohol -0.11149608945 0.46913030842951009
## 378          sq_drink_alcohol -0.04673299431 0.74326861358173835
## 379          sq_drink_alcohol  0.40955746795 0.00422640049175355
## 380  sq_average_drink_per_day -0.04042465331 0.87408657104043130
## 381  sq_average_drink_per_day -0.26009324830 0.53096833080426087
## 382  sq_average_drink_per_day -0.48147462903 0.00010257804469636
## 383             sq_self_hep_b -0.17885175294 0.11559742974387974
## 384             sq_self_hep_b  0.04522819244 0.85681327359605108
## 385             sq_self_hep_c -0.17786660733 0.11880220685086884
## 386             sq_self_hep_c  0.00515648437 0.98314670068817211
## 387         supp_meds_tylenol  0.44380109830 0.24563690847941486
## 388         supp_meds_tylenol  0.77050797170 0.21985255079337265
## 389        supp_meds_steroids  0.10780508159 0.75003621633080930
## 390        supp_meds_steroids -0.38080944008 0.71869901424882265
## 391             sq_water_well -0.18653694538 0.09675928899760877
## 392             sq_water_well -0.11223939809 0.46350632851723095
## 393   sq_water_tap_unfiltered -0.00717499280 0.95913876245625251
## 394   sq_water_tap_unfiltered  0.17549299403 0.18845245498274266
## 395 sq_water_house_filtration -0.16064803523 0.14111864812130245
## 396 sq_water_house_filtration -0.18414678866 0.31607063806374974
## 397    sq_water_faucet_filter -0.18099109271 0.13559656249832411
## 398    sq_water_faucet_filter -0.14435720063 0.26925087582704033
## 399  sq_water_charcoal_filter -0.17126417939 0.12499948414919104
## 400  sq_water_charcoal_filter -0.04422335533 0.77750970889267790
## 401          sq_water_bottled -0.43728254052 0.00182086074126584
## 402          sq_water_bottled -0.45962196552 0.00069540024129137
## 403             sq_water_none -0.16543176316 0.12150146864343667
## 404             sq_water_none -0.39284391304 0.10950059241961881
## 405       sq_water_other_type -0.18103712113 0.09537905447063832
## 406       sq_water_other_type -0.03766610528 0.85393082686462518
## 407                    source -0.59790103757 0.00005228868616012
## 408                    source -0.26557025946 0.03370576474755375
## 409                    source  0.32661198117 0.19403427471930992
## 410                       sex -0.15368685249 0.15386599131093590
## 411            race_eth_label  0.07231545684 0.77714097293777329
## 412            race_eth_label  0.47425865769 0.17483025510465880
## 413            race_eth_label  0.08698417548 0.72246058781372169
## 414            race_eth_label  0.45767860326 0.13554981673753960
## 415          race_final_label  0.34729696079 0.62303943070382883
## 416          race_final_label  1.65297624760 0.00107063992871887
## 417          race_final_label -0.07238566712 0.90034178273734011
## 418          race_final_label -0.04129806505 0.92002234501068081
## 419          race_final_label  0.06298670916 0.58422548881022029
## 420          race_final_label -0.25891127952 0.79509259208137861
## 421          race_final_label -0.20709863531 0.48201712804660735
## 422          race_final_label -0.22782367745 0.55058320024782048
## 423                 ethnicity  0.09994099180 0.67927159271312165
## 424                 ethnicity  0.45767860326 0.13566211156981897
## 425                     rural -0.14723144214 0.42822635251511909
## 426                     rural -0.26072761706 0.11000532282995749
## 427                   smoking  0.21611201277 0.23338823688726526
## 428                   smoking -0.06618776224 0.57137978178831528
## 429          sq_drink_alcohol  0.10156874252 0.51822776011725313
## 430          sq_drink_alcohol -0.00190253764 0.98957692131135044
## 431          sq_drink_alcohol  0.15143861403 0.29776677122813072
## 432  sq_average_drink_per_day  0.05748739112 0.82519940509489453
## 433  sq_average_drink_per_day -0.34492434674 0.41554963544339829
## 434  sq_average_drink_per_day -0.13243173333 0.29051139953655003
## 435             sq_self_hep_b -0.12772680791 0.26138986317014279
## 436             sq_self_hep_b -0.18926614933 0.45099558920554317
## 437             sq_self_hep_c -0.12436403200 0.27562127705553680
## 438             sq_self_hep_c -0.19167028106 0.43308498300754394
## 439         supp_meds_tylenol  0.25950328321 0.49765332661943340
## 440         supp_meds_tylenol  0.07771900517 0.90156876480204828
## 441        supp_meds_steroids  0.21785483823 0.51959775927009866
## 442        supp_meds_steroids -0.29015095263 0.78364104048900063
## 443             sq_water_well -0.03539646609 0.75308976935880034
## 444             sq_water_well -0.00403742287 0.97903005717147185
## 445   sq_water_tap_unfiltered -0.22763959852 0.10460564698569111
## 446   sq_water_tap_unfiltered -0.20965868166 0.11625097875513281
## 447 sq_water_house_filtration  0.03180003008 0.76900375265055310
## 448 sq_water_house_filtration  0.49963711198 0.00639538693209372
## 449    sq_water_faucet_filter -0.00731918832 0.95193128359767076
## 450    sq_water_faucet_filter  0.07300597137 0.57712410414268367
## 451  sq_water_charcoal_filter -0.02645028662 0.81297502605540750
## 452  sq_water_charcoal_filter  0.03855810651 0.80596269263470566
## 453          sq_water_bottled -0.12807664111 0.36598943670439332
## 454          sq_water_bottled -0.10002749761 0.46439951786494849
## 455             sq_water_none -0.00134633678 0.98995994083384609
## 456             sq_water_none -0.33307110634 0.17591982687334426
## 457       sq_water_other_type  0.02991785344 0.78311655900996846
## 458       sq_water_other_type  0.14932828872 0.46695697882131204
## 459                    source -0.12233567491 0.40741994614468358
## 460                    source  0.16665102186 0.18606871857653695
## 461                    source -0.71085759563 0.00529967763418086
## 462                       sex -0.08107283790 0.45234435016075591
## 463            race_eth_label  0.38843641214 0.12719466758624104
## 464            race_eth_label  0.75496304658 0.03031047612559701
## 465            race_eth_label  0.30938744827 0.20479634375196532
## 466            race_eth_label -0.06964204857 0.81923914578153800
## 467          race_final_label -0.54217003211 0.43999328372661040
## 468          race_final_label -0.49473597649 0.32117620165951011
## 469          race_final_label  0.31984176703 0.57762843834789201
## 470          race_final_label  1.39032924586 0.00073512345398631
## 471          race_final_label  0.08140436173 0.47660773266544987
## 472          race_final_label  0.53300229345 0.59053852024600917
## 473          race_final_label -0.22594287753 0.44010936421997648
## 474          race_final_label -0.56024015523 0.14005245576397632
## 475                 ethnicity  0.35330235643 0.14289994452075006
## 476                 ethnicity -0.06964204857 0.81947623415181903
## 477                     rural -0.21079073860 0.25793964453798679
## 478                     rural -0.04719442936 0.77247965322541445
## 479                   smoking -0.55938000641 0.00181874007453686
## 480                   smoking -0.32017764170 0.00560773476027302
## 481          sq_drink_alcohol -0.17786232689 0.24777442714207512
## 482          sq_drink_alcohol -0.16663937622 0.24258750496615397
## 483          sq_drink_alcohol  0.34004163394 0.01719459843915620
## 484  sq_average_drink_per_day  0.16709286937 0.51266621906039722
## 485  sq_average_drink_per_day  0.03513635302 0.93254748917459640
## 486  sq_average_drink_per_day -0.42766182037 0.00054676111079222
## 487             sq_self_hep_b -0.22785512960 0.04436660508739628
## 488             sq_self_hep_b  0.24387552625 0.32895313970867956
## 489             sq_self_hep_c -0.23110609461 0.04207291408647388
## 490             sq_self_hep_c  0.22064001249 0.36434293822168251
## 491         supp_meds_tylenol -0.32443230158 0.39565811530909123
## 492         supp_meds_tylenol  0.26527944634 0.67233383417726300
## 493        supp_meds_steroids -0.43690149238 0.19622409137501412
## 494        supp_meds_steroids -0.02186261809 0.98346705424688374
## 495             sq_water_well -0.12817120720 0.25427262322351152
## 496             sq_water_well -0.02495098293 0.87074988658916408
## 497   sq_water_tap_unfiltered -0.22205510583 0.11348990546404761
## 498   sq_water_tap_unfiltered -0.07001753613 0.59948647097832886
## 499 sq_water_house_filtration -0.13263834075 0.22481218952720550
## 500 sq_water_house_filtration -0.03978205473 0.82866854646978583
## 501    sq_water_faucet_filter -0.13144880015 0.27842603953196615
## 502    sq_water_faucet_filter  0.02976012029 0.81982321014451420
## 503  sq_water_charcoal_filter -0.11479817257 0.30392858519815302
## 504  sq_water_charcoal_filter  0.05286413942 0.73582611255727159
## 505          sq_water_bottled -0.35114193595 0.01283108050418380
## 506          sq_water_bottled -0.28139780657 0.03857335714816601
## 507             sq_water_none -0.09645572879 0.36794161779746204
## 508             sq_water_none -0.20912526088 0.39547290349843378
## 509       sq_water_other_type -0.13920849554 0.20007125981871401
## 510       sq_water_other_type -0.07811881114 0.70304722582391288
## 511                    source  0.06168004872 0.67844612713381902
## 512                    source  0.22832573243 0.07252736028874485
## 513                    source -0.45648133987 0.07474208092999160
## 514                       sex  0.30350051034 0.00469493718683770
## 515            race_eth_label  0.17144822696 0.50197497862570550
## 516            race_eth_label -0.08286614721 0.81223970154000491
## 517            race_eth_label  0.35914307931 0.14277455985685836
## 518            race_eth_label  0.17094081549 0.57643705503039200
## 519          race_final_label -0.35595703100 0.61712705883957342
## 520          race_final_label -0.75988071120 0.13339352193114282
## 521          race_final_label -0.06325872262 0.91352122741302155
## 522          race_final_label -0.37547025156 0.36528543014309145
## 523          race_final_label -0.16785923828 0.14835335109303602
## 524          race_final_label  1.00801708607 0.31601277293424263
## 525          race_final_label -0.25448828409 0.39135877967638943
## 526          race_final_label -0.27232894289 0.47898862867843928
## 527                 ethnicity  0.28253434133 0.24394937568137418
## 528                 ethnicity  0.17094081549 0.57772753504935903
## 529                     rural -0.15330859096 0.41080517756177970
## 530                     rural -0.05287271038 0.74619014229260427
## 531                   smoking -0.59743152776 0.00092276549007707
## 532                   smoking -0.12179225914 0.29205421769068646
## 533          sq_drink_alcohol -0.06535445769 0.67660542380994215
## 534          sq_drink_alcohol  0.01175053534 0.93549031572167918
## 535          sq_drink_alcohol  0.20806866087 0.15152091369328727
## 536  sq_average_drink_per_day  0.01595902681 0.95098333148843639
## 537  sq_average_drink_per_day  0.06376641718 0.88000354530749192
## 538  sq_average_drink_per_day -0.21608972737 0.08417452030592833
## 539             sq_self_hep_b -0.02247278890 0.84347580470059136
## 540             sq_self_hep_b  0.14660302112 0.55989141502025297
## 541             sq_self_hep_c -0.02853443225 0.80272136125276106
## 542             sq_self_hep_c  0.02671288685 0.91313950657492171
## 543         supp_meds_tylenol  0.30269349191 0.42836384684072104
## 544         supp_meds_tylenol  0.78143889628 0.21353816457676289
## 545        supp_meds_steroids -0.10559143425 0.75503614022516952
## 546        supp_meds_steroids -0.46625437338 0.65925938965952646
## 547             sq_water_well -0.06777253946 0.54651262733604811
## 548             sq_water_well -0.15540013737 0.31136152736212841
## 549   sq_water_tap_unfiltered -0.00727570282 0.95872659269934801
## 550   sq_water_tap_unfiltered  0.02603227415 0.84574417416999270
## 551 sq_water_house_filtration -0.03605705995 0.74164992928474538
## 552 sq_water_house_filtration -0.01642026493 0.92895252444544685
## 553    sq_water_faucet_filter -0.07838889488 0.51835547757238953
## 554    sq_water_faucet_filter -0.12496215620 0.33973975540600310
## 555  sq_water_charcoal_filter -0.04281037697 0.70176869616976578
## 556  sq_water_charcoal_filter  0.02425610311 0.87718241871528879
## 557          sq_water_bottled -0.34343405338 0.01441692733043675
## 558          sq_water_bottled -0.43313180106 0.00143238137611300
## 559             sq_water_none -0.03958620288 0.71147423698221934
## 560             sq_water_none -0.32045363493 0.19292650115117382
## 561       sq_water_other_type -0.04449232868 0.68223041086397185
## 562       sq_water_other_type -0.16656898382 0.41705478475211033
## 563                    source  0.00341597575 0.98179146397574657
## 564                    source  0.04502069166 0.72434393181777690
## 565                    source -0.51200370343 0.04708117779278184
## 566                       sex -0.02359390772 0.82693773382376023
## 567            race_eth_label  0.06086368697 0.81187103175202535
## 568            race_eth_label  0.28356857907 0.41725220517750161
## 569            race_eth_label  0.17080174030 0.48604179615837562
## 570            race_eth_label -0.23994111834 0.43384263785675281
## 571          race_final_label -0.46259942334 0.51535911876491247
## 572          race_final_label -0.48699606048 0.33501616397590472
## 573          race_final_label  0.00662795737 0.99090900750855360
## 574          race_final_label  0.76233095269 0.06613992103282181
## 575          race_final_label -0.10757306829 0.35324429414612923
## 576          race_final_label -0.15142758139 0.88002788778056229
## 577          race_final_label -0.07631609842 0.79678214569844263
## 578          race_final_label -0.54040796213 0.15992207734304267
## 579                 ethnicity  0.14262137861 0.55500838863838109
## 580                 ethnicity -0.23994111834 0.43331808561261076
## 581                     rural -0.13286357000 0.47604978557855959
## 582                     rural  0.00406386978 0.98015694799679176
## 583                   smoking -0.33925333501 0.06089981157603477
## 584                   smoking -0.16043150025 0.16912075431887635
## 585          sq_drink_alcohol  0.03369684146 0.82844187593956276
## 586          sq_drink_alcohol  0.03928903694 0.78511449567257685
## 587          sq_drink_alcohol  0.37799051426 0.00887878150753327
## 588  sq_average_drink_per_day -0.14649866148 0.56947537328377917
## 589  sq_average_drink_per_day -0.00615415023 0.98827941703331035
## 590  sq_average_drink_per_day -0.37744411216 0.00245565678435985
## 591             sq_self_hep_b -0.14105158765 0.21493465638586645
## 592             sq_self_hep_b  0.04210692449 0.86677177612829825
## 593             sq_self_hep_c -0.09979353088 0.37976264387495307
## 594             sq_self_hep_c  0.42840676793 0.07911871955041586
## 595         supp_meds_tylenol -0.24174331541 0.52552310304422201
## 596         supp_meds_tylenol  0.74300215321 0.23522327501678239
## 597        supp_meds_steroids -0.21852365905 0.51797858027497301
## 598        supp_meds_steroids  0.71454274816 0.49868186690490035
## 599             sq_water_well  0.01168605994 0.91721458430984049
## 600             sq_water_well  0.12608602047 0.41156166219780010
## 601   sq_water_tap_unfiltered -0.09220049374 0.51184853848187739
## 602   sq_water_tap_unfiltered -0.07543539858 0.57279182355582148
## 603 sq_water_house_filtration -0.01192867507 0.91310090818639877
## 604 sq_water_house_filtration -0.12977075568 0.48093371950682218
## 605    sq_water_faucet_filter -0.02728505772 0.82219076468470564
## 606    sq_water_faucet_filter -0.09022857915 0.49076539977288947
## 607  sq_water_charcoal_filter  0.03682718974 0.74186846476577573
## 608  sq_water_charcoal_filter  0.04040313633 0.79688719547918652
## 609          sq_water_bottled -0.17773609857 0.20792533592251639
## 610          sq_water_bottled -0.27538320757 0.04357849187013037
## 611             sq_water_none  0.02229770786 0.83524455521134733
## 612             sq_water_none -0.12918736057 0.59997849432490002
## 613       sq_water_other_type -0.06032328779 0.57889504928244273
## 614       sq_water_other_type  0.09236555500 0.65262891764171804
## 615                    source  0.12846146544 0.38687468700134564
## 616                    source  0.39720909646 0.00181891699048696
## 617                    source  0.04155554741 0.87053458291733876
## 618                       sex  0.08119651991 0.45165441374253601
## 619            race_eth_label -0.00586181941 0.98166500714231375
## 620            race_eth_label  0.07238360316 0.83548591495916558
## 621            race_eth_label  0.27902406267 0.25418843077719411
## 622            race_eth_label  0.04716044722 0.87739425723636211
## 623          race_final_label  1.36028732641 0.05400990074290221
## 624          race_final_label -0.67792247840 0.17574960546629939
## 625          race_final_label -0.05066729394 0.92993187406726230
## 626          race_final_label -0.43348449368 0.29078243295853851
## 627          race_final_label -0.30480826458 0.00818424840147474
## 628          race_final_label  0.21183589124 0.83120895797991856
## 629          race_final_label -0.36639427441 0.21245914434554627
## 630          race_final_label -0.37159837289 0.32895717410028713
## 631                 ethnicity  0.18359567650 0.44910052978292048
## 632                 ethnicity  0.04716044722 0.87801175679233379
## 633                     rural -0.29369835283 0.11397432465805926
## 634                     rural -0.22595188987 0.16528201759409997
## 635                   smoking -0.32812133861 0.07025007004597797
## 636                   smoking -0.08647518574 0.45886310938747255
## 637          sq_drink_alcohol  0.00872138962 0.95557605731020900
## 638          sq_drink_alcohol  0.05329752766 0.71336276014425226
## 639          sq_drink_alcohol  0.26418668784 0.06864932704252635
## 640  sq_average_drink_per_day  0.34598625878 0.18167914444426966
## 641  sq_average_drink_per_day -0.07706509184 0.85474769829995156
## 642  sq_average_drink_per_day -0.18716258000 0.13320907616193359
## 643             sq_self_hep_b -0.04384126872 0.70010452722554173
## 644             sq_self_hep_b -0.14852356441 0.55475236942286110
## 645             sq_self_hep_c -0.00850483650 0.94062361844988573
## 646             sq_self_hep_c -0.13325302642 0.58632012495494368
## 647         supp_meds_tylenol  0.18150755550 0.63535705748587723
## 648         supp_meds_tylenol  0.16741359119 0.79002340503005719
## 649        supp_meds_steroids  0.16256745203 0.63085743718607123
## 650        supp_meds_steroids -0.40299902397 0.70298916766313346
## 651             sq_water_well -0.03177598964 0.77734871293318042
## 652             sq_water_well -0.16152694531 0.29269446086609541
## 653   sq_water_tap_unfiltered  0.13088726281 0.34954163790135262
## 654   sq_water_tap_unfiltered  0.26544081020 0.04667168825859383
## 655 sq_water_house_filtration  0.03539716070 0.74609488302110494
## 656 sq_water_house_filtration  0.12286695845 0.50458013295510473
## 657    sq_water_faucet_filter  0.07422498204 0.54112145438894954
## 658    sq_water_faucet_filter  0.04600188469 0.72532178061484442
## 659  sq_water_charcoal_filter -0.01701590491 0.87904866822130390
## 660  sq_water_charcoal_filter -0.00714787774 0.96368572588700163
## 661          sq_water_bottled -0.30337952011 0.02943808106098152
## 662          sq_water_bottled -0.52713535708 0.00009910794049467
## 663             sq_water_none  0.03220906487 0.76347440250852572
## 664             sq_water_none -0.28637917545 0.24456352714546090
## 665       sq_water_other_type -0.00069820403 0.99487761581831213
## 666       sq_water_other_type  0.04767553387 0.81640595739469646
## 667                    source  0.51882522422 0.00035296753016782
## 668                    source  0.27329328029 0.02652537691163277
## 669                    source  1.36562416595 0.00000006218287011
## 670                       sex -0.15256425529 0.15690319949754505
## 671            race_eth_label -0.09961913738 0.69832721067772541
## 672            race_eth_label -0.28068732935 0.42434022335421961
## 673            race_eth_label -0.15502571296 0.52926880834615642
## 674            race_eth_label -0.24017983164 0.43570815119399686
## 675          race_final_label  0.36314576107 0.60936886944372626
## 676          race_final_label -0.12549586113 0.80359047025394681
## 677          race_final_label  0.08595421817 0.88247237807532541
## 678          race_final_label -0.55771131397 0.17818156233011151
## 679          race_final_label  0.05714150773 0.62161421926663007
## 680          race_final_label  2.15432621489 0.03221506802430414
## 681          race_final_label -0.13260894822 0.65440808798253147
## 682          race_final_label  0.32256034411 0.40092858744280124
## 683                 ethnicity -0.14391451569 0.55309406174542874
## 684                 ethnicity -0.24017983164 0.43478718661802029
## 685                     rural -0.03072806294 0.86877802201807885
## 686                     rural  0.22724981485 0.16396371383201136
## 687                   smoking  0.38382346020 0.03156290235866580
## 688                   smoking  0.42443224230 0.00024963802756887
## 689          sq_drink_alcohol -0.10651007576 0.49296709044419185
## 690          sq_drink_alcohol  0.29694661582 0.03963369285390450
## 691          sq_drink_alcohol -0.08549871620 0.55172036530077662
## 692  sq_average_drink_per_day -0.10442762134 0.68793945814009794
## 693  sq_average_drink_per_day -0.27319094465 0.51845922867124128
## 694  sq_average_drink_per_day  0.13982610455 0.26388573205592636
## 695             sq_self_hep_b  0.33867951731 0.00270288931958967
## 696             sq_self_hep_b -0.23822011787 0.33688740607011625
## 697             sq_self_hep_c  0.38630395960 0.00064482875448686
## 698             sq_self_hep_c  0.33973550067 0.15897129077815536
## 699         supp_meds_tylenol -0.03979105231 0.91717244608161719
## 700         supp_meds_tylenol -0.35438704003 0.57295049357703465
## 701        supp_meds_steroids  0.17348130782 0.60813457682162686
## 702        supp_meds_steroids  0.65707774780 0.53424295732899529
## 703             sq_water_well  0.28458588928 0.01091883956148439
## 704             sq_water_well -0.05692071246 0.70803138909271612
## 705   sq_water_tap_unfiltered  0.37268296635 0.00767897644492330
## 706   sq_water_tap_unfiltered  0.13244034482 0.31755980881213985
## 707 sq_water_house_filtration  0.27652632056 0.01100469577269840
## 708 sq_water_house_filtration -0.06234680346 0.73243980970183031
## 709    sq_water_faucet_filter  0.33122239842 0.00611511609349293
## 710    sq_water_faucet_filter  0.09641517113 0.45709065759293344
## 711  sq_water_charcoal_filter  0.28947527111 0.00910866204875724
## 712  sq_water_charcoal_filter -0.08575238784 0.58051667684539865
## 713          sq_water_bottled  0.32964303863 0.01932347445562221
## 714          sq_water_bottled  0.06993004302 0.60590779765292924
## 715             sq_water_none  0.27947592083 0.00867219569146533
## 716             sq_water_none -0.22926941595 0.34669199788674454
## 717       sq_water_other_type  0.33721476837 0.00169010311525126
## 718       sq_water_other_type  0.56525519790 0.00524748730281680
## 719                    source -0.50659899172 0.00049247622953313
## 720                    source -0.71331858138 0.00000001380182137
## 721                    source -0.67687749138 0.00655977315886617
## 722                       sex  0.31907869439 0.00293897166877725
## 723            race_eth_label -0.38456128103 0.13013569457742086
## 724            race_eth_label -0.01545994530 0.96442794455498793
## 725            race_eth_label -0.17734261188 0.46598140195591820
## 726            race_eth_label  0.28233582862 0.35335867587746195
## 727          race_final_label  1.13561169417 0.10806768285307439
## 728          race_final_label -0.42530906633 0.39610942244147207
## 729          race_final_label -0.47144851746 0.41428300467104739
## 730          race_final_label  0.30627546779 0.45604501729654012
## 731          race_final_label -0.30743253810 0.00776078864512350
## 732          race_final_label -0.47144851746 0.63586430273091588
## 733          race_final_label -0.42074996522 0.15305394925530741
## 734          race_final_label -0.16756226591 0.66018676901762929
## 735                 ethnicity -0.23273436513 0.33347886020210904
## 736                 ethnicity  0.28233582862 0.35448966539951243
## 737                     rural -0.01757076775 0.92489389560955348
## 738                     rural -0.10951764199 0.50284536393231494
## 739                   smoking -0.17985647561 0.32177091020604093
## 740                   smoking -0.07253382596 0.53558707695853414
## 741          sq_drink_alcohol  0.32796977117 0.03594188193302610
## 742          sq_drink_alcohol  0.02472820551 0.86408706078347330
## 743          sq_drink_alcohol -0.06718987230 0.64121543008337678
## 744  sq_average_drink_per_day -0.27041239489 0.29799235545930247
## 745  sq_average_drink_per_day  0.22209416367 0.59912099850931455
## 746  sq_average_drink_per_day  0.13900622351 0.26608530903584476
## 747             sq_self_hep_b -0.08054960079 0.47899561405698610
## 748             sq_self_hep_b  0.12386342952 0.62208712770531249
## 749             sq_self_hep_c -0.03332494094 0.77046630225326396
## 750             sq_self_hep_c -0.01353349626 0.95592885498327729
## 751         supp_meds_tylenol  0.07478577777 0.84501740098412648
## 752         supp_meds_tylenol -0.27064094184 0.66676216096642582
## 753        supp_meds_steroids  0.14678690410 0.66450267863284707
## 754        supp_meds_steroids -0.05528643906 0.95829689341887170
## 755             sq_water_well -0.07113127787 0.52721094260777346
## 756             sq_water_well -0.02027140684 0.89496451658350551
## 757   sq_water_tap_unfiltered -0.08114647657 0.56352426626744490
## 758   sq_water_tap_unfiltered -0.11994252123 0.36983338632042684
## 759 sq_water_house_filtration -0.02996935033 0.78307622374691188
## 760 sq_water_house_filtration  0.32265583066 0.07896650237649259
## 761    sq_water_faucet_filter  0.09829713530 0.41138610856268598
## 762    sq_water_faucet_filter  0.42992940577 0.00093424336603878
## 763  sq_water_charcoal_filter -0.12033262226 0.27988058283521206
## 764  sq_water_charcoal_filter -0.28953458010 0.06445662344530483
## 765          sq_water_bottled -0.10346737984 0.46520637575138080
## 766          sq_water_bottled -0.10752866806 0.43170112368953550
## 767             sq_water_none -0.01805025876 0.86604968749351308
## 768             sq_water_none  0.31162948316 0.20541041566927390
## 769       sq_water_other_type -0.06912266856 0.52440830804744287
## 770       sq_water_other_type -0.22972534384 0.26277924482141085
##                                                   Factor         PFAS
## 1                                      age_at_enrollment pf_hx_s_scld
## 2                                                    bmi pf_hx_s_scld
## 3                                            trig_mg_d_l pf_hx_s_scld
## 4                                      age_at_enrollment    pfda_scld
## 5                                                    bmi    pfda_scld
## 6                                            trig_mg_d_l    pfda_scld
## 7                                      age_at_enrollment    pfna_scld
## 8                                                    bmi    pfna_scld
## 9                                            trig_mg_d_l    pfna_scld
## 10                                     age_at_enrollment    pfos_scld
## 11                                                   bmi    pfos_scld
## 12                                           trig_mg_d_l    pfos_scld
## 13                                     age_at_enrollment pf_hp_a_scld
## 14                                                   bmi pf_hp_a_scld
## 15                                           trig_mg_d_l pf_hp_a_scld
## 16                                     age_at_enrollment    pfbs_scld
## 17                                                   bmi    pfbs_scld
## 18                                           trig_mg_d_l    pfbs_scld
## 19                                     age_at_enrollment    pfoa_scld
## 20                                                   bmi    pfoa_scld
## 21                                           trig_mg_d_l    pfoa_scld
## 22                                     age_at_enrollment pf_pe_a_scld
## 23                                                   bmi pf_pe_a_scld
## 24                                           trig_mg_d_l pf_pe_a_scld
## 25                                     age_at_enrollment pf_un_a_scld
## 26                                                   bmi pf_un_a_scld
## 27                                           trig_mg_d_l pf_un_a_scld
## 28                                     age_at_enrollment pf_hp_s_scld
## 29                                                   bmi pf_hp_s_scld
## 30                                           trig_mg_d_l pf_hp_s_scld
## 31                                     age_at_enrollment pf_do_a_scld
## 32                                                   bmi pf_do_a_scld
## 33                                           trig_mg_d_l pf_do_a_scld
## 34                                     age_at_enrollment pf_pe_s_scld
## 35                                                   bmi pf_pe_s_scld
## 36                                           trig_mg_d_l pf_pe_s_scld
## 37                                     age_at_enrollment pf_hx_a_scld
## 38                                                   bmi pf_hx_a_scld
## 39                                           trig_mg_d_l pf_hx_a_scld
## 40                                     age_at_enrollment    pfba_scld
## 41                                                   bmi    pfba_scld
## 42                                           trig_mg_d_l    pfba_scld
## 43                                            sourceDUKE pf_hx_s_scld
## 44                                            sourceNCSU pf_hx_s_scld
## 45                                             sourceUNC pf_hx_s_scld
## 46                                               sexMale pf_hx_s_scld
## 47                                     race_eth_labelNHB pf_hx_s_scld
## 48                                     race_eth_labelNHO pf_hx_s_scld
## 49                                     race_eth_labelNHW pf_hx_s_scld
## 50                    race_eth_labelUnknown/Not Reported pf_hx_s_scld
## 51                       race_final_labelAmerican Indian pf_hx_s_scld
## 52        race_final_labelAmerican Indian/Alaskan Native pf_hx_s_scld
## 53                                 race_final_labelAsian pf_hx_s_scld
## 54                race_final_labelAsian/Pacific Islander pf_hx_s_scld
## 55                                 race_final_labelBlack pf_hx_s_scld
## 56                    race_final_labelMore than one race pf_hx_s_scld
## 57                                 race_final_labelOther pf_hx_s_scld
## 58                  race_final_labelUnknown/Not Reported pf_hx_s_scld
## 59                                 ethnicityNot Hispanic pf_hx_s_scld
## 60                         ethnicityUnknown/Not Reported pf_hx_s_scld
## 61                             ruralLiving in rural area pf_hx_s_scld
## 62                             ruralUnknown/Not Reported pf_hx_s_scld
## 63                              smokingSmoke or use vape pf_hx_s_scld
## 64                           smokingUnknown/Not Reported pf_hx_s_scld
## 65          sq_drink_alcoholNo, former drinker (stopped) pf_hx_s_scld
## 66                  sq_drink_alcoholUnknown/Not Reported pf_hx_s_scld
## 67                  sq_drink_alcoholYes, current drinker pf_hx_s_scld
## 68  sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_s_scld
## 69  sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_s_scld
## 70          sq_average_drink_per_dayUnknown/Not Reported pf_hx_s_scld
## 71                     sq_self_hep_bUnknown/Not Reported pf_hx_s_scld
## 72                                      sq_self_hep_bYes pf_hx_s_scld
## 73                     sq_self_hep_cUnknown/Not Reported pf_hx_s_scld
## 74                                      sq_self_hep_cYes pf_hx_s_scld
## 75                 supp_meds_tylenolUnknown/Not Reported pf_hx_s_scld
## 76                                  supp_meds_tylenolYes pf_hx_s_scld
## 77                supp_meds_steroidsUnknown/Not Reported pf_hx_s_scld
## 78                                 supp_meds_steroidsYes pf_hx_s_scld
## 79                     sq_water_wellUnknown/Not Reported pf_hx_s_scld
## 80                                      sq_water_wellYes pf_hx_s_scld
## 81           sq_water_tap_unfilteredUnknown/Not Reported pf_hx_s_scld
## 82                            sq_water_tap_unfilteredYes pf_hx_s_scld
## 83         sq_water_house_filtrationUnknown/Not Reported pf_hx_s_scld
## 84                          sq_water_house_filtrationYes pf_hx_s_scld
## 85            sq_water_faucet_filterUnknown/Not Reported pf_hx_s_scld
## 86                             sq_water_faucet_filterYes pf_hx_s_scld
## 87          sq_water_charcoal_filterUnknown/Not Reported pf_hx_s_scld
## 88                           sq_water_charcoal_filterYes pf_hx_s_scld
## 89                  sq_water_bottledUnknown/Not Reported pf_hx_s_scld
## 90                                   sq_water_bottledYes pf_hx_s_scld
## 91                     sq_water_noneUnknown/Not Reported pf_hx_s_scld
## 92                                      sq_water_noneYes pf_hx_s_scld
## 93               sq_water_other_typeUnknown/Not Reported pf_hx_s_scld
## 94                                sq_water_other_typeYes pf_hx_s_scld
## 95                                            sourceDUKE    pfda_scld
## 96                                            sourceNCSU    pfda_scld
## 97                                             sourceUNC    pfda_scld
## 98                                               sexMale    pfda_scld
## 99                                     race_eth_labelNHB    pfda_scld
## 100                                    race_eth_labelNHO    pfda_scld
## 101                                    race_eth_labelNHW    pfda_scld
## 102                   race_eth_labelUnknown/Not Reported    pfda_scld
## 103                      race_final_labelAmerican Indian    pfda_scld
## 104       race_final_labelAmerican Indian/Alaskan Native    pfda_scld
## 105                                race_final_labelAsian    pfda_scld
## 106               race_final_labelAsian/Pacific Islander    pfda_scld
## 107                                race_final_labelBlack    pfda_scld
## 108                   race_final_labelMore than one race    pfda_scld
## 109                                race_final_labelOther    pfda_scld
## 110                 race_final_labelUnknown/Not Reported    pfda_scld
## 111                                ethnicityNot Hispanic    pfda_scld
## 112                        ethnicityUnknown/Not Reported    pfda_scld
## 113                            ruralLiving in rural area    pfda_scld
## 114                            ruralUnknown/Not Reported    pfda_scld
## 115                             smokingSmoke or use vape    pfda_scld
## 116                          smokingUnknown/Not Reported    pfda_scld
## 117         sq_drink_alcoholNo, former drinker (stopped)    pfda_scld
## 118                 sq_drink_alcoholUnknown/Not Reported    pfda_scld
## 119                 sq_drink_alcoholYes, current drinker    pfda_scld
## 120 sq_average_drink_per_day1-2 alcoholic drinks per day    pfda_scld
## 121 sq_average_drink_per_day3-4 alcoholic drinks per day    pfda_scld
## 122         sq_average_drink_per_dayUnknown/Not Reported    pfda_scld
## 123                    sq_self_hep_bUnknown/Not Reported    pfda_scld
## 124                                     sq_self_hep_bYes    pfda_scld
## 125                    sq_self_hep_cUnknown/Not Reported    pfda_scld
## 126                                     sq_self_hep_cYes    pfda_scld
## 127                supp_meds_tylenolUnknown/Not Reported    pfda_scld
## 128                                 supp_meds_tylenolYes    pfda_scld
## 129               supp_meds_steroidsUnknown/Not Reported    pfda_scld
## 130                                supp_meds_steroidsYes    pfda_scld
## 131                    sq_water_wellUnknown/Not Reported    pfda_scld
## 132                                     sq_water_wellYes    pfda_scld
## 133          sq_water_tap_unfilteredUnknown/Not Reported    pfda_scld
## 134                           sq_water_tap_unfilteredYes    pfda_scld
## 135        sq_water_house_filtrationUnknown/Not Reported    pfda_scld
## 136                         sq_water_house_filtrationYes    pfda_scld
## 137           sq_water_faucet_filterUnknown/Not Reported    pfda_scld
## 138                            sq_water_faucet_filterYes    pfda_scld
## 139         sq_water_charcoal_filterUnknown/Not Reported    pfda_scld
## 140                          sq_water_charcoal_filterYes    pfda_scld
## 141                 sq_water_bottledUnknown/Not Reported    pfda_scld
## 142                                  sq_water_bottledYes    pfda_scld
## 143                    sq_water_noneUnknown/Not Reported    pfda_scld
## 144                                     sq_water_noneYes    pfda_scld
## 145              sq_water_other_typeUnknown/Not Reported    pfda_scld
## 146                               sq_water_other_typeYes    pfda_scld
## 147                                           sourceDUKE    pfna_scld
## 148                                           sourceNCSU    pfna_scld
## 149                                            sourceUNC    pfna_scld
## 150                                              sexMale    pfna_scld
## 151                                    race_eth_labelNHB    pfna_scld
## 152                                    race_eth_labelNHO    pfna_scld
## 153                                    race_eth_labelNHW    pfna_scld
## 154                   race_eth_labelUnknown/Not Reported    pfna_scld
## 155                      race_final_labelAmerican Indian    pfna_scld
## 156       race_final_labelAmerican Indian/Alaskan Native    pfna_scld
## 157                                race_final_labelAsian    pfna_scld
## 158               race_final_labelAsian/Pacific Islander    pfna_scld
## 159                                race_final_labelBlack    pfna_scld
## 160                   race_final_labelMore than one race    pfna_scld
## 161                                race_final_labelOther    pfna_scld
## 162                 race_final_labelUnknown/Not Reported    pfna_scld
## 163                                ethnicityNot Hispanic    pfna_scld
## 164                        ethnicityUnknown/Not Reported    pfna_scld
## 165                            ruralLiving in rural area    pfna_scld
## 166                            ruralUnknown/Not Reported    pfna_scld
## 167                             smokingSmoke or use vape    pfna_scld
## 168                          smokingUnknown/Not Reported    pfna_scld
## 169         sq_drink_alcoholNo, former drinker (stopped)    pfna_scld
## 170                 sq_drink_alcoholUnknown/Not Reported    pfna_scld
## 171                 sq_drink_alcoholYes, current drinker    pfna_scld
## 172 sq_average_drink_per_day1-2 alcoholic drinks per day    pfna_scld
## 173 sq_average_drink_per_day3-4 alcoholic drinks per day    pfna_scld
## 174         sq_average_drink_per_dayUnknown/Not Reported    pfna_scld
## 175                    sq_self_hep_bUnknown/Not Reported    pfna_scld
## 176                                     sq_self_hep_bYes    pfna_scld
## 177                    sq_self_hep_cUnknown/Not Reported    pfna_scld
## 178                                     sq_self_hep_cYes    pfna_scld
## 179                supp_meds_tylenolUnknown/Not Reported    pfna_scld
## 180                                 supp_meds_tylenolYes    pfna_scld
## 181               supp_meds_steroidsUnknown/Not Reported    pfna_scld
## 182                                supp_meds_steroidsYes    pfna_scld
## 183                    sq_water_wellUnknown/Not Reported    pfna_scld
## 184                                     sq_water_wellYes    pfna_scld
## 185          sq_water_tap_unfilteredUnknown/Not Reported    pfna_scld
## 186                           sq_water_tap_unfilteredYes    pfna_scld
## 187        sq_water_house_filtrationUnknown/Not Reported    pfna_scld
## 188                         sq_water_house_filtrationYes    pfna_scld
## 189           sq_water_faucet_filterUnknown/Not Reported    pfna_scld
## 190                            sq_water_faucet_filterYes    pfna_scld
## 191         sq_water_charcoal_filterUnknown/Not Reported    pfna_scld
## 192                          sq_water_charcoal_filterYes    pfna_scld
## 193                 sq_water_bottledUnknown/Not Reported    pfna_scld
## 194                                  sq_water_bottledYes    pfna_scld
## 195                    sq_water_noneUnknown/Not Reported    pfna_scld
## 196                                     sq_water_noneYes    pfna_scld
## 197              sq_water_other_typeUnknown/Not Reported    pfna_scld
## 198                               sq_water_other_typeYes    pfna_scld
## 199                                           sourceDUKE    pfos_scld
## 200                                           sourceNCSU    pfos_scld
## 201                                            sourceUNC    pfos_scld
## 202                                              sexMale    pfos_scld
## 203                                    race_eth_labelNHB    pfos_scld
## 204                                    race_eth_labelNHO    pfos_scld
## 205                                    race_eth_labelNHW    pfos_scld
## 206                   race_eth_labelUnknown/Not Reported    pfos_scld
## 207                      race_final_labelAmerican Indian    pfos_scld
## 208       race_final_labelAmerican Indian/Alaskan Native    pfos_scld
## 209                                race_final_labelAsian    pfos_scld
## 210               race_final_labelAsian/Pacific Islander    pfos_scld
## 211                                race_final_labelBlack    pfos_scld
## 212                   race_final_labelMore than one race    pfos_scld
## 213                                race_final_labelOther    pfos_scld
## 214                 race_final_labelUnknown/Not Reported    pfos_scld
## 215                                ethnicityNot Hispanic    pfos_scld
## 216                        ethnicityUnknown/Not Reported    pfos_scld
## 217                            ruralLiving in rural area    pfos_scld
## 218                            ruralUnknown/Not Reported    pfos_scld
## 219                             smokingSmoke or use vape    pfos_scld
## 220                          smokingUnknown/Not Reported    pfos_scld
## 221         sq_drink_alcoholNo, former drinker (stopped)    pfos_scld
## 222                 sq_drink_alcoholUnknown/Not Reported    pfos_scld
## 223                 sq_drink_alcoholYes, current drinker    pfos_scld
## 224 sq_average_drink_per_day1-2 alcoholic drinks per day    pfos_scld
## 225 sq_average_drink_per_day3-4 alcoholic drinks per day    pfos_scld
## 226         sq_average_drink_per_dayUnknown/Not Reported    pfos_scld
## 227                    sq_self_hep_bUnknown/Not Reported    pfos_scld
## 228                                     sq_self_hep_bYes    pfos_scld
## 229                    sq_self_hep_cUnknown/Not Reported    pfos_scld
## 230                                     sq_self_hep_cYes    pfos_scld
## 231                supp_meds_tylenolUnknown/Not Reported    pfos_scld
## 232                                 supp_meds_tylenolYes    pfos_scld
## 233               supp_meds_steroidsUnknown/Not Reported    pfos_scld
## 234                                supp_meds_steroidsYes    pfos_scld
## 235                    sq_water_wellUnknown/Not Reported    pfos_scld
## 236                                     sq_water_wellYes    pfos_scld
## 237          sq_water_tap_unfilteredUnknown/Not Reported    pfos_scld
## 238                           sq_water_tap_unfilteredYes    pfos_scld
## 239        sq_water_house_filtrationUnknown/Not Reported    pfos_scld
## 240                         sq_water_house_filtrationYes    pfos_scld
## 241           sq_water_faucet_filterUnknown/Not Reported    pfos_scld
## 242                            sq_water_faucet_filterYes    pfos_scld
## 243         sq_water_charcoal_filterUnknown/Not Reported    pfos_scld
## 244                          sq_water_charcoal_filterYes    pfos_scld
## 245                 sq_water_bottledUnknown/Not Reported    pfos_scld
## 246                                  sq_water_bottledYes    pfos_scld
## 247                    sq_water_noneUnknown/Not Reported    pfos_scld
## 248                                     sq_water_noneYes    pfos_scld
## 249              sq_water_other_typeUnknown/Not Reported    pfos_scld
## 250                               sq_water_other_typeYes    pfos_scld
## 251                                           sourceDUKE pf_hp_a_scld
## 252                                           sourceNCSU pf_hp_a_scld
## 253                                            sourceUNC pf_hp_a_scld
## 254                                              sexMale pf_hp_a_scld
## 255                                    race_eth_labelNHB pf_hp_a_scld
## 256                                    race_eth_labelNHO pf_hp_a_scld
## 257                                    race_eth_labelNHW pf_hp_a_scld
## 258                   race_eth_labelUnknown/Not Reported pf_hp_a_scld
## 259                      race_final_labelAmerican Indian pf_hp_a_scld
## 260       race_final_labelAmerican Indian/Alaskan Native pf_hp_a_scld
## 261                                race_final_labelAsian pf_hp_a_scld
## 262               race_final_labelAsian/Pacific Islander pf_hp_a_scld
## 263                                race_final_labelBlack pf_hp_a_scld
## 264                   race_final_labelMore than one race pf_hp_a_scld
## 265                                race_final_labelOther pf_hp_a_scld
## 266                 race_final_labelUnknown/Not Reported pf_hp_a_scld
## 267                                ethnicityNot Hispanic pf_hp_a_scld
## 268                        ethnicityUnknown/Not Reported pf_hp_a_scld
## 269                            ruralLiving in rural area pf_hp_a_scld
## 270                            ruralUnknown/Not Reported pf_hp_a_scld
## 271                             smokingSmoke or use vape pf_hp_a_scld
## 272                          smokingUnknown/Not Reported pf_hp_a_scld
## 273         sq_drink_alcoholNo, former drinker (stopped) pf_hp_a_scld
## 274                 sq_drink_alcoholUnknown/Not Reported pf_hp_a_scld
## 275                 sq_drink_alcoholYes, current drinker pf_hp_a_scld
## 276 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_a_scld
## 277 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_a_scld
## 278         sq_average_drink_per_dayUnknown/Not Reported pf_hp_a_scld
## 279                    sq_self_hep_bUnknown/Not Reported pf_hp_a_scld
## 280                                     sq_self_hep_bYes pf_hp_a_scld
## 281                    sq_self_hep_cUnknown/Not Reported pf_hp_a_scld
## 282                                     sq_self_hep_cYes pf_hp_a_scld
## 283                supp_meds_tylenolUnknown/Not Reported pf_hp_a_scld
## 284                                 supp_meds_tylenolYes pf_hp_a_scld
## 285               supp_meds_steroidsUnknown/Not Reported pf_hp_a_scld
## 286                                supp_meds_steroidsYes pf_hp_a_scld
## 287                    sq_water_wellUnknown/Not Reported pf_hp_a_scld
## 288                                     sq_water_wellYes pf_hp_a_scld
## 289          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_a_scld
## 290                           sq_water_tap_unfilteredYes pf_hp_a_scld
## 291        sq_water_house_filtrationUnknown/Not Reported pf_hp_a_scld
## 292                         sq_water_house_filtrationYes pf_hp_a_scld
## 293           sq_water_faucet_filterUnknown/Not Reported pf_hp_a_scld
## 294                            sq_water_faucet_filterYes pf_hp_a_scld
## 295         sq_water_charcoal_filterUnknown/Not Reported pf_hp_a_scld
## 296                          sq_water_charcoal_filterYes pf_hp_a_scld
## 297                 sq_water_bottledUnknown/Not Reported pf_hp_a_scld
## 298                                  sq_water_bottledYes pf_hp_a_scld
## 299                    sq_water_noneUnknown/Not Reported pf_hp_a_scld
## 300                                     sq_water_noneYes pf_hp_a_scld
## 301              sq_water_other_typeUnknown/Not Reported pf_hp_a_scld
## 302                               sq_water_other_typeYes pf_hp_a_scld
## 303                                           sourceDUKE    pfbs_scld
## 304                                           sourceNCSU    pfbs_scld
## 305                                            sourceUNC    pfbs_scld
## 306                                              sexMale    pfbs_scld
## 307                                    race_eth_labelNHB    pfbs_scld
## 308                                    race_eth_labelNHO    pfbs_scld
## 309                                    race_eth_labelNHW    pfbs_scld
## 310                   race_eth_labelUnknown/Not Reported    pfbs_scld
## 311                      race_final_labelAmerican Indian    pfbs_scld
## 312       race_final_labelAmerican Indian/Alaskan Native    pfbs_scld
## 313                                race_final_labelAsian    pfbs_scld
## 314               race_final_labelAsian/Pacific Islander    pfbs_scld
## 315                                race_final_labelBlack    pfbs_scld
## 316                   race_final_labelMore than one race    pfbs_scld
## 317                                race_final_labelOther    pfbs_scld
## 318                 race_final_labelUnknown/Not Reported    pfbs_scld
## 319                                ethnicityNot Hispanic    pfbs_scld
## 320                        ethnicityUnknown/Not Reported    pfbs_scld
## 321                            ruralLiving in rural area    pfbs_scld
## 322                            ruralUnknown/Not Reported    pfbs_scld
## 323                             smokingSmoke or use vape    pfbs_scld
## 324                          smokingUnknown/Not Reported    pfbs_scld
## 325         sq_drink_alcoholNo, former drinker (stopped)    pfbs_scld
## 326                 sq_drink_alcoholUnknown/Not Reported    pfbs_scld
## 327                 sq_drink_alcoholYes, current drinker    pfbs_scld
## 328 sq_average_drink_per_day1-2 alcoholic drinks per day    pfbs_scld
## 329 sq_average_drink_per_day3-4 alcoholic drinks per day    pfbs_scld
## 330         sq_average_drink_per_dayUnknown/Not Reported    pfbs_scld
## 331                    sq_self_hep_bUnknown/Not Reported    pfbs_scld
## 332                                     sq_self_hep_bYes    pfbs_scld
## 333                    sq_self_hep_cUnknown/Not Reported    pfbs_scld
## 334                                     sq_self_hep_cYes    pfbs_scld
## 335                supp_meds_tylenolUnknown/Not Reported    pfbs_scld
## 336                                 supp_meds_tylenolYes    pfbs_scld
## 337               supp_meds_steroidsUnknown/Not Reported    pfbs_scld
## 338                                supp_meds_steroidsYes    pfbs_scld
## 339                    sq_water_wellUnknown/Not Reported    pfbs_scld
## 340                                     sq_water_wellYes    pfbs_scld
## 341          sq_water_tap_unfilteredUnknown/Not Reported    pfbs_scld
## 342                           sq_water_tap_unfilteredYes    pfbs_scld
## 343        sq_water_house_filtrationUnknown/Not Reported    pfbs_scld
## 344                         sq_water_house_filtrationYes    pfbs_scld
## 345           sq_water_faucet_filterUnknown/Not Reported    pfbs_scld
## 346                            sq_water_faucet_filterYes    pfbs_scld
## 347         sq_water_charcoal_filterUnknown/Not Reported    pfbs_scld
## 348                          sq_water_charcoal_filterYes    pfbs_scld
## 349                 sq_water_bottledUnknown/Not Reported    pfbs_scld
## 350                                  sq_water_bottledYes    pfbs_scld
## 351                    sq_water_noneUnknown/Not Reported    pfbs_scld
## 352                                     sq_water_noneYes    pfbs_scld
## 353              sq_water_other_typeUnknown/Not Reported    pfbs_scld
## 354                               sq_water_other_typeYes    pfbs_scld
## 355                                           sourceDUKE    pfoa_scld
## 356                                           sourceNCSU    pfoa_scld
## 357                                            sourceUNC    pfoa_scld
## 358                                              sexMale    pfoa_scld
## 359                                    race_eth_labelNHB    pfoa_scld
## 360                                    race_eth_labelNHO    pfoa_scld
## 361                                    race_eth_labelNHW    pfoa_scld
## 362                   race_eth_labelUnknown/Not Reported    pfoa_scld
## 363                      race_final_labelAmerican Indian    pfoa_scld
## 364       race_final_labelAmerican Indian/Alaskan Native    pfoa_scld
## 365                                race_final_labelAsian    pfoa_scld
## 366               race_final_labelAsian/Pacific Islander    pfoa_scld
## 367                                race_final_labelBlack    pfoa_scld
## 368                   race_final_labelMore than one race    pfoa_scld
## 369                                race_final_labelOther    pfoa_scld
## 370                 race_final_labelUnknown/Not Reported    pfoa_scld
## 371                                ethnicityNot Hispanic    pfoa_scld
## 372                        ethnicityUnknown/Not Reported    pfoa_scld
## 373                            ruralLiving in rural area    pfoa_scld
## 374                            ruralUnknown/Not Reported    pfoa_scld
## 375                             smokingSmoke or use vape    pfoa_scld
## 376                          smokingUnknown/Not Reported    pfoa_scld
## 377         sq_drink_alcoholNo, former drinker (stopped)    pfoa_scld
## 378                 sq_drink_alcoholUnknown/Not Reported    pfoa_scld
## 379                 sq_drink_alcoholYes, current drinker    pfoa_scld
## 380 sq_average_drink_per_day1-2 alcoholic drinks per day    pfoa_scld
## 381 sq_average_drink_per_day3-4 alcoholic drinks per day    pfoa_scld
## 382         sq_average_drink_per_dayUnknown/Not Reported    pfoa_scld
## 383                    sq_self_hep_bUnknown/Not Reported    pfoa_scld
## 384                                     sq_self_hep_bYes    pfoa_scld
## 385                    sq_self_hep_cUnknown/Not Reported    pfoa_scld
## 386                                     sq_self_hep_cYes    pfoa_scld
## 387                supp_meds_tylenolUnknown/Not Reported    pfoa_scld
## 388                                 supp_meds_tylenolYes    pfoa_scld
## 389               supp_meds_steroidsUnknown/Not Reported    pfoa_scld
## 390                                supp_meds_steroidsYes    pfoa_scld
## 391                    sq_water_wellUnknown/Not Reported    pfoa_scld
## 392                                     sq_water_wellYes    pfoa_scld
## 393          sq_water_tap_unfilteredUnknown/Not Reported    pfoa_scld
## 394                           sq_water_tap_unfilteredYes    pfoa_scld
## 395        sq_water_house_filtrationUnknown/Not Reported    pfoa_scld
## 396                         sq_water_house_filtrationYes    pfoa_scld
## 397           sq_water_faucet_filterUnknown/Not Reported    pfoa_scld
## 398                            sq_water_faucet_filterYes    pfoa_scld
## 399         sq_water_charcoal_filterUnknown/Not Reported    pfoa_scld
## 400                          sq_water_charcoal_filterYes    pfoa_scld
## 401                 sq_water_bottledUnknown/Not Reported    pfoa_scld
## 402                                  sq_water_bottledYes    pfoa_scld
## 403                    sq_water_noneUnknown/Not Reported    pfoa_scld
## 404                                     sq_water_noneYes    pfoa_scld
## 405              sq_water_other_typeUnknown/Not Reported    pfoa_scld
## 406                               sq_water_other_typeYes    pfoa_scld
## 407                                           sourceDUKE pf_pe_a_scld
## 408                                           sourceNCSU pf_pe_a_scld
## 409                                            sourceUNC pf_pe_a_scld
## 410                                              sexMale pf_pe_a_scld
## 411                                    race_eth_labelNHB pf_pe_a_scld
## 412                                    race_eth_labelNHO pf_pe_a_scld
## 413                                    race_eth_labelNHW pf_pe_a_scld
## 414                   race_eth_labelUnknown/Not Reported pf_pe_a_scld
## 415                      race_final_labelAmerican Indian pf_pe_a_scld
## 416       race_final_labelAmerican Indian/Alaskan Native pf_pe_a_scld
## 417                                race_final_labelAsian pf_pe_a_scld
## 418               race_final_labelAsian/Pacific Islander pf_pe_a_scld
## 419                                race_final_labelBlack pf_pe_a_scld
## 420                   race_final_labelMore than one race pf_pe_a_scld
## 421                                race_final_labelOther pf_pe_a_scld
## 422                 race_final_labelUnknown/Not Reported pf_pe_a_scld
## 423                                ethnicityNot Hispanic pf_pe_a_scld
## 424                        ethnicityUnknown/Not Reported pf_pe_a_scld
## 425                            ruralLiving in rural area pf_pe_a_scld
## 426                            ruralUnknown/Not Reported pf_pe_a_scld
## 427                             smokingSmoke or use vape pf_pe_a_scld
## 428                          smokingUnknown/Not Reported pf_pe_a_scld
## 429         sq_drink_alcoholNo, former drinker (stopped) pf_pe_a_scld
## 430                 sq_drink_alcoholUnknown/Not Reported pf_pe_a_scld
## 431                 sq_drink_alcoholYes, current drinker pf_pe_a_scld
## 432 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_a_scld
## 433 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_a_scld
## 434         sq_average_drink_per_dayUnknown/Not Reported pf_pe_a_scld
## 435                    sq_self_hep_bUnknown/Not Reported pf_pe_a_scld
## 436                                     sq_self_hep_bYes pf_pe_a_scld
## 437                    sq_self_hep_cUnknown/Not Reported pf_pe_a_scld
## 438                                     sq_self_hep_cYes pf_pe_a_scld
## 439                supp_meds_tylenolUnknown/Not Reported pf_pe_a_scld
## 440                                 supp_meds_tylenolYes pf_pe_a_scld
## 441               supp_meds_steroidsUnknown/Not Reported pf_pe_a_scld
## 442                                supp_meds_steroidsYes pf_pe_a_scld
## 443                    sq_water_wellUnknown/Not Reported pf_pe_a_scld
## 444                                     sq_water_wellYes pf_pe_a_scld
## 445          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_a_scld
## 446                           sq_water_tap_unfilteredYes pf_pe_a_scld
## 447        sq_water_house_filtrationUnknown/Not Reported pf_pe_a_scld
## 448                         sq_water_house_filtrationYes pf_pe_a_scld
## 449           sq_water_faucet_filterUnknown/Not Reported pf_pe_a_scld
## 450                            sq_water_faucet_filterYes pf_pe_a_scld
## 451         sq_water_charcoal_filterUnknown/Not Reported pf_pe_a_scld
## 452                          sq_water_charcoal_filterYes pf_pe_a_scld
## 453                 sq_water_bottledUnknown/Not Reported pf_pe_a_scld
## 454                                  sq_water_bottledYes pf_pe_a_scld
## 455                    sq_water_noneUnknown/Not Reported pf_pe_a_scld
## 456                                     sq_water_noneYes pf_pe_a_scld
## 457              sq_water_other_typeUnknown/Not Reported pf_pe_a_scld
## 458                               sq_water_other_typeYes pf_pe_a_scld
## 459                                           sourceDUKE pf_un_a_scld
## 460                                           sourceNCSU pf_un_a_scld
## 461                                            sourceUNC pf_un_a_scld
## 462                                              sexMale pf_un_a_scld
## 463                                    race_eth_labelNHB pf_un_a_scld
## 464                                    race_eth_labelNHO pf_un_a_scld
## 465                                    race_eth_labelNHW pf_un_a_scld
## 466                   race_eth_labelUnknown/Not Reported pf_un_a_scld
## 467                      race_final_labelAmerican Indian pf_un_a_scld
## 468       race_final_labelAmerican Indian/Alaskan Native pf_un_a_scld
## 469                                race_final_labelAsian pf_un_a_scld
## 470               race_final_labelAsian/Pacific Islander pf_un_a_scld
## 471                                race_final_labelBlack pf_un_a_scld
## 472                   race_final_labelMore than one race pf_un_a_scld
## 473                                race_final_labelOther pf_un_a_scld
## 474                 race_final_labelUnknown/Not Reported pf_un_a_scld
## 475                                ethnicityNot Hispanic pf_un_a_scld
## 476                        ethnicityUnknown/Not Reported pf_un_a_scld
## 477                            ruralLiving in rural area pf_un_a_scld
## 478                            ruralUnknown/Not Reported pf_un_a_scld
## 479                             smokingSmoke or use vape pf_un_a_scld
## 480                          smokingUnknown/Not Reported pf_un_a_scld
## 481         sq_drink_alcoholNo, former drinker (stopped) pf_un_a_scld
## 482                 sq_drink_alcoholUnknown/Not Reported pf_un_a_scld
## 483                 sq_drink_alcoholYes, current drinker pf_un_a_scld
## 484 sq_average_drink_per_day1-2 alcoholic drinks per day pf_un_a_scld
## 485 sq_average_drink_per_day3-4 alcoholic drinks per day pf_un_a_scld
## 486         sq_average_drink_per_dayUnknown/Not Reported pf_un_a_scld
## 487                    sq_self_hep_bUnknown/Not Reported pf_un_a_scld
## 488                                     sq_self_hep_bYes pf_un_a_scld
## 489                    sq_self_hep_cUnknown/Not Reported pf_un_a_scld
## 490                                     sq_self_hep_cYes pf_un_a_scld
## 491                supp_meds_tylenolUnknown/Not Reported pf_un_a_scld
## 492                                 supp_meds_tylenolYes pf_un_a_scld
## 493               supp_meds_steroidsUnknown/Not Reported pf_un_a_scld
## 494                                supp_meds_steroidsYes pf_un_a_scld
## 495                    sq_water_wellUnknown/Not Reported pf_un_a_scld
## 496                                     sq_water_wellYes pf_un_a_scld
## 497          sq_water_tap_unfilteredUnknown/Not Reported pf_un_a_scld
## 498                           sq_water_tap_unfilteredYes pf_un_a_scld
## 499        sq_water_house_filtrationUnknown/Not Reported pf_un_a_scld
## 500                         sq_water_house_filtrationYes pf_un_a_scld
## 501           sq_water_faucet_filterUnknown/Not Reported pf_un_a_scld
## 502                            sq_water_faucet_filterYes pf_un_a_scld
## 503         sq_water_charcoal_filterUnknown/Not Reported pf_un_a_scld
## 504                          sq_water_charcoal_filterYes pf_un_a_scld
## 505                 sq_water_bottledUnknown/Not Reported pf_un_a_scld
## 506                                  sq_water_bottledYes pf_un_a_scld
## 507                    sq_water_noneUnknown/Not Reported pf_un_a_scld
## 508                                     sq_water_noneYes pf_un_a_scld
## 509              sq_water_other_typeUnknown/Not Reported pf_un_a_scld
## 510                               sq_water_other_typeYes pf_un_a_scld
## 511                                           sourceDUKE pf_hp_s_scld
## 512                                           sourceNCSU pf_hp_s_scld
## 513                                            sourceUNC pf_hp_s_scld
## 514                                              sexMale pf_hp_s_scld
## 515                                    race_eth_labelNHB pf_hp_s_scld
## 516                                    race_eth_labelNHO pf_hp_s_scld
## 517                                    race_eth_labelNHW pf_hp_s_scld
## 518                   race_eth_labelUnknown/Not Reported pf_hp_s_scld
## 519                      race_final_labelAmerican Indian pf_hp_s_scld
## 520       race_final_labelAmerican Indian/Alaskan Native pf_hp_s_scld
## 521                                race_final_labelAsian pf_hp_s_scld
## 522               race_final_labelAsian/Pacific Islander pf_hp_s_scld
## 523                                race_final_labelBlack pf_hp_s_scld
## 524                   race_final_labelMore than one race pf_hp_s_scld
## 525                                race_final_labelOther pf_hp_s_scld
## 526                 race_final_labelUnknown/Not Reported pf_hp_s_scld
## 527                                ethnicityNot Hispanic pf_hp_s_scld
## 528                        ethnicityUnknown/Not Reported pf_hp_s_scld
## 529                            ruralLiving in rural area pf_hp_s_scld
## 530                            ruralUnknown/Not Reported pf_hp_s_scld
## 531                             smokingSmoke or use vape pf_hp_s_scld
## 532                          smokingUnknown/Not Reported pf_hp_s_scld
## 533         sq_drink_alcoholNo, former drinker (stopped) pf_hp_s_scld
## 534                 sq_drink_alcoholUnknown/Not Reported pf_hp_s_scld
## 535                 sq_drink_alcoholYes, current drinker pf_hp_s_scld
## 536 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hp_s_scld
## 537 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hp_s_scld
## 538         sq_average_drink_per_dayUnknown/Not Reported pf_hp_s_scld
## 539                    sq_self_hep_bUnknown/Not Reported pf_hp_s_scld
## 540                                     sq_self_hep_bYes pf_hp_s_scld
## 541                    sq_self_hep_cUnknown/Not Reported pf_hp_s_scld
## 542                                     sq_self_hep_cYes pf_hp_s_scld
## 543                supp_meds_tylenolUnknown/Not Reported pf_hp_s_scld
## 544                                 supp_meds_tylenolYes pf_hp_s_scld
## 545               supp_meds_steroidsUnknown/Not Reported pf_hp_s_scld
## 546                                supp_meds_steroidsYes pf_hp_s_scld
## 547                    sq_water_wellUnknown/Not Reported pf_hp_s_scld
## 548                                     sq_water_wellYes pf_hp_s_scld
## 549          sq_water_tap_unfilteredUnknown/Not Reported pf_hp_s_scld
## 550                           sq_water_tap_unfilteredYes pf_hp_s_scld
## 551        sq_water_house_filtrationUnknown/Not Reported pf_hp_s_scld
## 552                         sq_water_house_filtrationYes pf_hp_s_scld
## 553           sq_water_faucet_filterUnknown/Not Reported pf_hp_s_scld
## 554                            sq_water_faucet_filterYes pf_hp_s_scld
## 555         sq_water_charcoal_filterUnknown/Not Reported pf_hp_s_scld
## 556                          sq_water_charcoal_filterYes pf_hp_s_scld
## 557                 sq_water_bottledUnknown/Not Reported pf_hp_s_scld
## 558                                  sq_water_bottledYes pf_hp_s_scld
## 559                    sq_water_noneUnknown/Not Reported pf_hp_s_scld
## 560                                     sq_water_noneYes pf_hp_s_scld
## 561              sq_water_other_typeUnknown/Not Reported pf_hp_s_scld
## 562                               sq_water_other_typeYes pf_hp_s_scld
## 563                                           sourceDUKE pf_do_a_scld
## 564                                           sourceNCSU pf_do_a_scld
## 565                                            sourceUNC pf_do_a_scld
## 566                                              sexMale pf_do_a_scld
## 567                                    race_eth_labelNHB pf_do_a_scld
## 568                                    race_eth_labelNHO pf_do_a_scld
## 569                                    race_eth_labelNHW pf_do_a_scld
## 570                   race_eth_labelUnknown/Not Reported pf_do_a_scld
## 571                      race_final_labelAmerican Indian pf_do_a_scld
## 572       race_final_labelAmerican Indian/Alaskan Native pf_do_a_scld
## 573                                race_final_labelAsian pf_do_a_scld
## 574               race_final_labelAsian/Pacific Islander pf_do_a_scld
## 575                                race_final_labelBlack pf_do_a_scld
## 576                   race_final_labelMore than one race pf_do_a_scld
## 577                                race_final_labelOther pf_do_a_scld
## 578                 race_final_labelUnknown/Not Reported pf_do_a_scld
## 579                                ethnicityNot Hispanic pf_do_a_scld
## 580                        ethnicityUnknown/Not Reported pf_do_a_scld
## 581                            ruralLiving in rural area pf_do_a_scld
## 582                            ruralUnknown/Not Reported pf_do_a_scld
## 583                             smokingSmoke or use vape pf_do_a_scld
## 584                          smokingUnknown/Not Reported pf_do_a_scld
## 585         sq_drink_alcoholNo, former drinker (stopped) pf_do_a_scld
## 586                 sq_drink_alcoholUnknown/Not Reported pf_do_a_scld
## 587                 sq_drink_alcoholYes, current drinker pf_do_a_scld
## 588 sq_average_drink_per_day1-2 alcoholic drinks per day pf_do_a_scld
## 589 sq_average_drink_per_day3-4 alcoholic drinks per day pf_do_a_scld
## 590         sq_average_drink_per_dayUnknown/Not Reported pf_do_a_scld
## 591                    sq_self_hep_bUnknown/Not Reported pf_do_a_scld
## 592                                     sq_self_hep_bYes pf_do_a_scld
## 593                    sq_self_hep_cUnknown/Not Reported pf_do_a_scld
## 594                                     sq_self_hep_cYes pf_do_a_scld
## 595                supp_meds_tylenolUnknown/Not Reported pf_do_a_scld
## 596                                 supp_meds_tylenolYes pf_do_a_scld
## 597               supp_meds_steroidsUnknown/Not Reported pf_do_a_scld
## 598                                supp_meds_steroidsYes pf_do_a_scld
## 599                    sq_water_wellUnknown/Not Reported pf_do_a_scld
## 600                                     sq_water_wellYes pf_do_a_scld
## 601          sq_water_tap_unfilteredUnknown/Not Reported pf_do_a_scld
## 602                           sq_water_tap_unfilteredYes pf_do_a_scld
## 603        sq_water_house_filtrationUnknown/Not Reported pf_do_a_scld
## 604                         sq_water_house_filtrationYes pf_do_a_scld
## 605           sq_water_faucet_filterUnknown/Not Reported pf_do_a_scld
## 606                            sq_water_faucet_filterYes pf_do_a_scld
## 607         sq_water_charcoal_filterUnknown/Not Reported pf_do_a_scld
## 608                          sq_water_charcoal_filterYes pf_do_a_scld
## 609                 sq_water_bottledUnknown/Not Reported pf_do_a_scld
## 610                                  sq_water_bottledYes pf_do_a_scld
## 611                    sq_water_noneUnknown/Not Reported pf_do_a_scld
## 612                                     sq_water_noneYes pf_do_a_scld
## 613              sq_water_other_typeUnknown/Not Reported pf_do_a_scld
## 614                               sq_water_other_typeYes pf_do_a_scld
## 615                                           sourceDUKE pf_pe_s_scld
## 616                                           sourceNCSU pf_pe_s_scld
## 617                                            sourceUNC pf_pe_s_scld
## 618                                              sexMale pf_pe_s_scld
## 619                                    race_eth_labelNHB pf_pe_s_scld
## 620                                    race_eth_labelNHO pf_pe_s_scld
## 621                                    race_eth_labelNHW pf_pe_s_scld
## 622                   race_eth_labelUnknown/Not Reported pf_pe_s_scld
## 623                      race_final_labelAmerican Indian pf_pe_s_scld
## 624       race_final_labelAmerican Indian/Alaskan Native pf_pe_s_scld
## 625                                race_final_labelAsian pf_pe_s_scld
## 626               race_final_labelAsian/Pacific Islander pf_pe_s_scld
## 627                                race_final_labelBlack pf_pe_s_scld
## 628                   race_final_labelMore than one race pf_pe_s_scld
## 629                                race_final_labelOther pf_pe_s_scld
## 630                 race_final_labelUnknown/Not Reported pf_pe_s_scld
## 631                                ethnicityNot Hispanic pf_pe_s_scld
## 632                        ethnicityUnknown/Not Reported pf_pe_s_scld
## 633                            ruralLiving in rural area pf_pe_s_scld
## 634                            ruralUnknown/Not Reported pf_pe_s_scld
## 635                             smokingSmoke or use vape pf_pe_s_scld
## 636                          smokingUnknown/Not Reported pf_pe_s_scld
## 637         sq_drink_alcoholNo, former drinker (stopped) pf_pe_s_scld
## 638                 sq_drink_alcoholUnknown/Not Reported pf_pe_s_scld
## 639                 sq_drink_alcoholYes, current drinker pf_pe_s_scld
## 640 sq_average_drink_per_day1-2 alcoholic drinks per day pf_pe_s_scld
## 641 sq_average_drink_per_day3-4 alcoholic drinks per day pf_pe_s_scld
## 642         sq_average_drink_per_dayUnknown/Not Reported pf_pe_s_scld
## 643                    sq_self_hep_bUnknown/Not Reported pf_pe_s_scld
## 644                                     sq_self_hep_bYes pf_pe_s_scld
## 645                    sq_self_hep_cUnknown/Not Reported pf_pe_s_scld
## 646                                     sq_self_hep_cYes pf_pe_s_scld
## 647                supp_meds_tylenolUnknown/Not Reported pf_pe_s_scld
## 648                                 supp_meds_tylenolYes pf_pe_s_scld
## 649               supp_meds_steroidsUnknown/Not Reported pf_pe_s_scld
## 650                                supp_meds_steroidsYes pf_pe_s_scld
## 651                    sq_water_wellUnknown/Not Reported pf_pe_s_scld
## 652                                     sq_water_wellYes pf_pe_s_scld
## 653          sq_water_tap_unfilteredUnknown/Not Reported pf_pe_s_scld
## 654                           sq_water_tap_unfilteredYes pf_pe_s_scld
## 655        sq_water_house_filtrationUnknown/Not Reported pf_pe_s_scld
## 656                         sq_water_house_filtrationYes pf_pe_s_scld
## 657           sq_water_faucet_filterUnknown/Not Reported pf_pe_s_scld
## 658                            sq_water_faucet_filterYes pf_pe_s_scld
## 659         sq_water_charcoal_filterUnknown/Not Reported pf_pe_s_scld
## 660                          sq_water_charcoal_filterYes pf_pe_s_scld
## 661                 sq_water_bottledUnknown/Not Reported pf_pe_s_scld
## 662                                  sq_water_bottledYes pf_pe_s_scld
## 663                    sq_water_noneUnknown/Not Reported pf_pe_s_scld
## 664                                     sq_water_noneYes pf_pe_s_scld
## 665              sq_water_other_typeUnknown/Not Reported pf_pe_s_scld
## 666                               sq_water_other_typeYes pf_pe_s_scld
## 667                                           sourceDUKE pf_hx_a_scld
## 668                                           sourceNCSU pf_hx_a_scld
## 669                                            sourceUNC pf_hx_a_scld
## 670                                              sexMale pf_hx_a_scld
## 671                                    race_eth_labelNHB pf_hx_a_scld
## 672                                    race_eth_labelNHO pf_hx_a_scld
## 673                                    race_eth_labelNHW pf_hx_a_scld
## 674                   race_eth_labelUnknown/Not Reported pf_hx_a_scld
## 675                      race_final_labelAmerican Indian pf_hx_a_scld
## 676       race_final_labelAmerican Indian/Alaskan Native pf_hx_a_scld
## 677                                race_final_labelAsian pf_hx_a_scld
## 678               race_final_labelAsian/Pacific Islander pf_hx_a_scld
## 679                                race_final_labelBlack pf_hx_a_scld
## 680                   race_final_labelMore than one race pf_hx_a_scld
## 681                                race_final_labelOther pf_hx_a_scld
## 682                 race_final_labelUnknown/Not Reported pf_hx_a_scld
## 683                                ethnicityNot Hispanic pf_hx_a_scld
## 684                        ethnicityUnknown/Not Reported pf_hx_a_scld
## 685                            ruralLiving in rural area pf_hx_a_scld
## 686                            ruralUnknown/Not Reported pf_hx_a_scld
## 687                             smokingSmoke or use vape pf_hx_a_scld
## 688                          smokingUnknown/Not Reported pf_hx_a_scld
## 689         sq_drink_alcoholNo, former drinker (stopped) pf_hx_a_scld
## 690                 sq_drink_alcoholUnknown/Not Reported pf_hx_a_scld
## 691                 sq_drink_alcoholYes, current drinker pf_hx_a_scld
## 692 sq_average_drink_per_day1-2 alcoholic drinks per day pf_hx_a_scld
## 693 sq_average_drink_per_day3-4 alcoholic drinks per day pf_hx_a_scld
## 694         sq_average_drink_per_dayUnknown/Not Reported pf_hx_a_scld
## 695                    sq_self_hep_bUnknown/Not Reported pf_hx_a_scld
## 696                                     sq_self_hep_bYes pf_hx_a_scld
## 697                    sq_self_hep_cUnknown/Not Reported pf_hx_a_scld
## 698                                     sq_self_hep_cYes pf_hx_a_scld
## 699                supp_meds_tylenolUnknown/Not Reported pf_hx_a_scld
## 700                                 supp_meds_tylenolYes pf_hx_a_scld
## 701               supp_meds_steroidsUnknown/Not Reported pf_hx_a_scld
## 702                                supp_meds_steroidsYes pf_hx_a_scld
## 703                    sq_water_wellUnknown/Not Reported pf_hx_a_scld
## 704                                     sq_water_wellYes pf_hx_a_scld
## 705          sq_water_tap_unfilteredUnknown/Not Reported pf_hx_a_scld
## 706                           sq_water_tap_unfilteredYes pf_hx_a_scld
## 707        sq_water_house_filtrationUnknown/Not Reported pf_hx_a_scld
## 708                         sq_water_house_filtrationYes pf_hx_a_scld
## 709           sq_water_faucet_filterUnknown/Not Reported pf_hx_a_scld
## 710                            sq_water_faucet_filterYes pf_hx_a_scld
## 711         sq_water_charcoal_filterUnknown/Not Reported pf_hx_a_scld
## 712                          sq_water_charcoal_filterYes pf_hx_a_scld
## 713                 sq_water_bottledUnknown/Not Reported pf_hx_a_scld
## 714                                  sq_water_bottledYes pf_hx_a_scld
## 715                    sq_water_noneUnknown/Not Reported pf_hx_a_scld
## 716                                     sq_water_noneYes pf_hx_a_scld
## 717              sq_water_other_typeUnknown/Not Reported pf_hx_a_scld
## 718                               sq_water_other_typeYes pf_hx_a_scld
## 719                                           sourceDUKE    pfba_scld
## 720                                           sourceNCSU    pfba_scld
## 721                                            sourceUNC    pfba_scld
## 722                                              sexMale    pfba_scld
## 723                                    race_eth_labelNHB    pfba_scld
## 724                                    race_eth_labelNHO    pfba_scld
## 725                                    race_eth_labelNHW    pfba_scld
## 726                   race_eth_labelUnknown/Not Reported    pfba_scld
## 727                      race_final_labelAmerican Indian    pfba_scld
## 728       race_final_labelAmerican Indian/Alaskan Native    pfba_scld
## 729                                race_final_labelAsian    pfba_scld
## 730               race_final_labelAsian/Pacific Islander    pfba_scld
## 731                                race_final_labelBlack    pfba_scld
## 732                   race_final_labelMore than one race    pfba_scld
## 733                                race_final_labelOther    pfba_scld
## 734                 race_final_labelUnknown/Not Reported    pfba_scld
## 735                                ethnicityNot Hispanic    pfba_scld
## 736                        ethnicityUnknown/Not Reported    pfba_scld
## 737                            ruralLiving in rural area    pfba_scld
## 738                            ruralUnknown/Not Reported    pfba_scld
## 739                             smokingSmoke or use vape    pfba_scld
## 740                          smokingUnknown/Not Reported    pfba_scld
## 741         sq_drink_alcoholNo, former drinker (stopped)    pfba_scld
## 742                 sq_drink_alcoholUnknown/Not Reported    pfba_scld
## 743                 sq_drink_alcoholYes, current drinker    pfba_scld
## 744 sq_average_drink_per_day1-2 alcoholic drinks per day    pfba_scld
## 745 sq_average_drink_per_day3-4 alcoholic drinks per day    pfba_scld
## 746         sq_average_drink_per_dayUnknown/Not Reported    pfba_scld
## 747                    sq_self_hep_bUnknown/Not Reported    pfba_scld
## 748                                     sq_self_hep_bYes    pfba_scld
## 749                    sq_self_hep_cUnknown/Not Reported    pfba_scld
## 750                                     sq_self_hep_cYes    pfba_scld
## 751                supp_meds_tylenolUnknown/Not Reported    pfba_scld
## 752                                 supp_meds_tylenolYes    pfba_scld
## 753               supp_meds_steroidsUnknown/Not Reported    pfba_scld
## 754                                supp_meds_steroidsYes    pfba_scld
## 755                    sq_water_wellUnknown/Not Reported    pfba_scld
## 756                                     sq_water_wellYes    pfba_scld
## 757          sq_water_tap_unfilteredUnknown/Not Reported    pfba_scld
## 758                           sq_water_tap_unfilteredYes    pfba_scld
## 759        sq_water_house_filtrationUnknown/Not Reported    pfba_scld
## 760                         sq_water_house_filtrationYes    pfba_scld
## 761           sq_water_faucet_filterUnknown/Not Reported    pfba_scld
## 762                            sq_water_faucet_filterYes    pfba_scld
## 763         sq_water_charcoal_filterUnknown/Not Reported    pfba_scld
## 764                          sq_water_charcoal_filterYes    pfba_scld
## 765                 sq_water_bottledUnknown/Not Reported    pfba_scld
## 766                                  sq_water_bottledYes    pfba_scld
## 767                    sq_water_noneUnknown/Not Reported    pfba_scld
## 768                                     sq_water_noneYes    pfba_scld
## 769              sq_water_other_typeUnknown/Not Reported    pfba_scld
## 770                               sq_water_other_typeYes    pfba_scld
##
ggplot(all_results, aes(x = "", y = Coeff)) +
  geom_boxplot(fill = "lightblue", color = "blue") +
  labs(title = "Box Plot of Coefficients", y = "Coefficient") +
  theme_minimal()   

##


all_results <- all_results %>%
  mutate(

# Create a formatted table using gt
results_df %>%
  gt() %>%
  tab_header(
    title = "Associations between exposures and AST/ALT Ratio From Simple LR"
  ) %>%
  cols_label(
   
    term = "Variables",
    estimate = "Estimate",
    std.error = "Standard Error",
    statistic = "Statistic",
    p.value = "P-value"
  ) %>%
  fmt_number(
    columns = c(estimate, std.error, statistic, p.value),
    decimals = 3
  ) %>%
  tab_source_note(
    source_note = "Significant results with p-value < 0.05"
  )
Associations between exposures and AST/ALT Ratio From Simple LR
Variables Estimate Standard Error Statistic P-value
pf_hx_s −0.138 0.061 −2.264 0.024
pfda −0.327 0.301 −1.086 0.278
pfna −0.048 0.184 −0.262 0.794
pfos −0.045 0.015 −2.998 0.003
pf_hp_a 0.557 1.832 0.304 0.761
pfbs 0.000 0.914 0.000 1.000
pfoa −0.001 0.067 −0.020 0.984
pf_pe_a −0.096 3.570 −0.027 0.979
pf_un_a −1.029 0.677 −1.520 0.129
pf_hp_s −1.898 0.573 −3.314 0.001
pf_do_a −1.112 2.855 −0.390 0.697
pf_pe_s −5.312 3.166 −1.678 0.094
pf_hx_a 32.737 9.377 3.491 0.001
pfba −0.601 1.636 −0.368 0.713
sourceEmory 0.503 0.283 1.774 0.077
sourceNCSU −0.173 0.239 −0.721 0.471
sourceUNC 2.167 0.476 4.553 0.000
age_at_enrollment −0.001 0.011 −0.117 0.907
sexMale −0.369 0.209 −1.772 0.077
race_eth_labelNHB −0.033 0.504 −0.066 0.947
race_eth_labelNHO −0.488 0.682 −0.716 0.474
race_eth_labelNHW −0.227 0.484 −0.468 0.640
race_eth_labelUnknown/Not Reported −0.320 0.612 −0.523 0.601
race_final_labelAmerican Indian/Alaskan Native −1.860 1.693 −1.099 0.273
race_final_labelAsian −2.071 1.693 −1.223 0.222
race_final_labelAsian/Pacific Islander −1.180 1.514 −0.779 0.436
race_final_labelBlack −0.661 1.324 −0.499 0.618
race_final_labelMore than one race −1.982 2.271 −0.873 0.383
race_final_labelOther 0.325 1.425 0.228 0.820
race_final_labelUnknown/Not Reported −1.441 1.514 −0.952 0.342
race_final_labelWhite −0.823 1.317 −0.625 0.532
ethnicityNot Hispanic −0.181 0.477 −0.379 0.705
ethnicityUnknown/Not Reported −0.320 0.611 −0.524 0.601
ruralLiving in rural area −0.380 0.357 −1.065 0.288
ruralUnknown/Not Reported −0.064 0.318 −0.201 0.841
smokingSmoke or use vape 1.525 0.342 4.460 0.000
smokingUnknown/Not Reported −0.049 0.220 −0.222 0.825
sq_drink_alcoholNo, never drinker −0.381 0.306 −1.245 0.214
sq_drink_alcoholUnknown/Not Reported −0.536 0.289 −1.854 0.065
sq_drink_alcoholYes, current drinker −0.428 0.285 −1.499 0.135
sq_average_drink_per_day3-4 alcoholic drinks per day −0.005 0.878 −0.005 0.996
sq_average_drink_per_dayLess than 1 alcoholic drink per day 0.097 0.486 0.200 0.841
sq_average_drink_per_dayUnknown/Not Reported 0.170 0.455 0.373 0.709
sq_self_hep_bUnknown/Not Reported −0.144 0.219 −0.661 0.509
sq_self_hep_bYes 0.780 0.466 1.675 0.095
sq_self_hep_cUnknown/Not Reported −0.116 0.214 −0.541 0.589
sq_self_hep_cYes 1.926 0.455 4.228 0.000
supp_meds_tylenolUnknown/Not Reported −0.678 0.765 −0.886 0.376
supp_meds_tylenolYes −1.185 1.314 −0.902 0.368
supp_meds_steroidsUnknown/Not Reported −0.515 0.710 −0.725 0.469
supp_meds_steroidsYes −1.669 1.987 −0.840 0.402
sq_water_wellUnknown/Not Reported −0.191 0.217 −0.879 0.380
sq_water_wellYes 0.030 0.298 0.102 0.919
sq_water_tap_unfilteredUnknown/Not Reported −0.575 0.273 −2.106 0.036
sq_water_tap_unfilteredYes −0.336 0.258 −1.301 0.194
sq_water_house_filtrationUnknown/Not Reported −0.162 0.210 −0.771 0.441
sq_water_house_filtrationYes 0.376 0.356 1.056 0.291
sq_water_faucet_filterUnknown/Not Reported −0.333 0.234 −1.427 0.155
sq_water_faucet_filterYes −0.339 0.252 −1.345 0.179
sq_water_charcoal_filterUnknown/Not Reported −0.196 0.216 −0.908 0.364
sq_water_charcoal_filterYes −0.262 0.300 −0.871 0.384
sq_water_bottledUnknown/Not Reported −0.154 0.271 −0.566 0.572
sq_water_bottledYes 0.390 0.261 1.491 0.137
sq_water_noneUnknown/Not Reported −0.171 0.206 −0.829 0.408
sq_water_noneYes −0.272 0.498 −0.546 0.585
sq_water_other_typeUnknown/Not Reported −0.131 0.210 −0.624 0.533
sq_water_other_typeYes −0.443 0.382 −1.159 0.247
Significant results with p-value < 0.05